The Influence of POGIL and MFI Models on Science Literacy and Science Process Skills for Junior High School

: This study aims to determine how the influence of the POGIL model on the ability of the science process and scientific literacy of students, in addition to knowing how the MFI model affects science process skills and scientific literacy of students and the comparison of POGIL and MFI models on science process skills and scientific literacy of Lhokseumawe City Junior High School students. This research uses an approach to quantitative research. The population of this research is the seventh-grade students of SMP in Lhokseumawe city. The research sample taken is SMPN students Arun, SMPN 1 Lhokseumawe, and SMPN 2 Lhokseumawe every 3 schools that consist of 2 experimental classes (POGIL and MFI models) and 1 control class (conventional) with a total of 9 classes. The research instrument used is a test question for knowing students' literacy skills, practicum guide sheets, and observation sheets to determine students' science process skills. In the class with the POGIL model, the average value of scientific literacy ability is 32.27, and science process skills are 81.32. In class with the MFI model, the average value of science literacy skills is 26.84, and process skills science 88.71. While the control class has an average value of scientific literacy ability of 12.77 and science process skills of 68.54. Based on Test Test output Statistics data of students' scientific literacy and science process skills are known as the value of asymp. Sig. (2-tailed) of 0.00 < 0.05, it can be concluded that the POGIL model has a significant effect on students' scientific literacy and science process skills. The results for the MFI class scores of students' scientific literacy skills and science process skills, because of the Asymp value. Sig. (0.000) < (0.05) so that learning with the MFI model affects the scientific literacy ability and science process skills of students. Classes taught with POGIL and MFI models with Asymp grades. Sig. (0.099 > (0.05) means that there is no significant difference in students' scientific literacy skills between students who are taught with the POGIL model and students who are taught with the MFI model but there is a significant difference in science process skills of students with POGIL and MFI models, where the MFI model students' science process skills higher than the POGIL model.

Introduction
Science and technology are developing very rapidly in the era of the industrial revolution 4.0. In this era, technology and innovation are spreading faster and more widely than in previous eras. A society that can embrace technological innovation is the main key to the progress of a country (Philbeck & Davis, 2018). Therefore, preparing a generation that is qualified and able to master technological developments is important which is important for the future of the country. The 21st century is the century in which the countries of the world can interact with each other as if they had no geographical barriers. Science and technology are increasing rapidly. Globalization is rampant. Resources can flow freely and openly. The emergence of foreign workers who are free to enter a country, the number of imported products that affect the production system actions in the country, and so on. For this reason, Indonesia must have the competencies needed in the 21st century which can be obtained through the learning process.
Among these competencies, the first is science process skills. Science process skills are an important skill in carrying out scientific activities (Elfeky et al., 2020;Inayah et al., 2020;Ramayanti et al., 2017). Science process skills play a role in helping students develop their minds, participate actively, and build a sense of responsibility in the process of gaining or developing knowledge (Atmojo, 2012;Haryadi & Pujiastuti, 2020;Ongowo and Indoshi, 2013;Safaruddin et al., 2020). Science process skills can also help students to understand concepts (Prasasti & Listiani, 2018).
Science process skills are important for building knowledge and being applied in learning. Building knowledge is not only obtained through theory but can also be obtained from experimental activities or activitybased learning. This is where science process skills can emerge because these skills contain scientific methods or steps in seeking, gaining new knowledge, and developing existing knowledge. If students are directly trained to build their knowledge, that knowledge will last a long time in them (Suryaningsih, 2017;Yulkifli et al., 2019). The application of learning by improving science process skills is very important because of the rapid development of science, so teaching students verbally is no longer possible, students must be trained to seek knowledge, and discover new knowledge and concepts.
Science process skills are believed to improve scientific literacy (Handayani et al., 2018). Scientific literacy skills for students are needed. The literature in the field of science education also shows that scientific literacy is increasingly being accepted and valued by educators as an expected learning outcome (Rahayu, 2017). Scientific literacy is defined as the ability to engage in science-related issues and think about science as a wise citizen (OECD, 2016). Scientific literacy is needed to understand scientific issues, the benefits, and risks of science (Fasasi, 2017), and with scientific literacy skills can understand social and environmental problems faced by society in this modern era, especially those that rely on knowledge and technology (Turiman et al., 2012).
Scientific literacy can be measured through the PISA (Program for International Student Assessment) organized by the OECD (Organization for Economic Cooperation and Development) every three years. The results of the PISA study for the average scientific literacy ability of Indonesian students from 2000, 2003, 2006, 2009, 2012, 2015were 393, 395, 393, 385, 375, 403, and 396 (OECD, 2019. The results of students' scientific literacy are still in the low category because the scores obtained are below the average PISA completeness score. This indicates that Indonesian students have not been able to understand the concepts and processes of science and have not been able to apply the scientific knowledge they have learned in everyday life (Sutrisna, 2021). The dimensions of scientific literacy presented by the Program for International Student Assessment (PISA) consist of (1) context in the form of personal (self, family, and peer group), local/national (society), and global (life around the world). PISA divides the fields of science application into three groups, namely life and health, earth and the environment, and technology (2) competencies in the form of explaining scientific phenomena, evaluating and designing scientific investigations, interpreting scientific data and evidence, (3 ) scientific knowledge which consists of three dimensions, namely content knowledge, procedural knowledge, and epistemic knowledge (Nofiana and Julianto, 2018;OECD, 2016).
Factors that cause students' low scientific literacy include the selection of textbooks, misconceptions, noncontextual learning, and students' reading ability (Fuadi et al., 2020). The low scientific literacy ability of students shows that the students' higher-order thinking skills and cognitive strategies are still low. Higher-order thinking skills and cognitive strategies cannot be taught directly but must be trained in the learning process. Therefore, the factors causing the low scientific literacy ability of students can come from teachers, quality of learning and teaching, students, facilities and infrastructure, etc.
Teachers have a considerable influence on learning outcomes. The low scientific literacy ability of students can be caused because teachers tend to not integrate the concept of learning with its application in everyday life. This causes students to often have difficulty in solving the problems presented in the problem because they are not accustomed to linking the knowledge that has been obtained with real-life situations.
One solution to increase the ability of students' science process skills and scientific literacy is to apply the inquiry learning model which is one of the recommended learning models in the 2013 curriculum (Mardianti et al., 2020). Inquiry models that promote inquiry strategies and process skills are the Process Oriented Guided Inquiry Learning (POGIL) and Modified Free Inquiry (MFI) models.
POGIL is a process-oriented and student-centered inquiry learning model, designed with small groups interacting with instructors or teachers as facilitators (Cahayningrum et al., 2017). POGIL is a heterogeneous group learning model with the aim that students' mastery of the material can be improved to develop thinking, communication, discussion, analysis, and evaluation skills to realize the students' learning process correctly. The syntax of the POGIL learning model is direction, exploration, concept discovery, application, and closing (Alamanda, 2015). The MFI learning model is one type of inquiry learning model. MFI has the characteristics of the teacher as a facilitator, and motivator, and provides the necessary assistance to ensure the smooth learning process of students (Aqib and Murtadlo, 2016). Where the teacher only gives problems, students are asked to solve them through observation, exploration, or through research procedures. The teacher acts as a motivator, and resource person, and is tasked with assisting when students need it. With this model, students are directed to explore, design, and perform experiments.
The influence of the inquiry learning model to improve science process skills and scientific literacy has been widely carried out by academics around the world, including in Indonesia. Research results reveal different values expressed in terms of education level, media used, type of inquiry, and subjects used. Based on this, researchers are interested in knowing how the POGIL model influences students' science process skills and scientific literacy, in addition to knowing how the MFI model influences students' science process skills and scientific literacy and the comparison of POGIL and MFI models on science process skills and scientific literacy. Lhokseumawe City Junior High School students.

Method
This research uses a quantitative research approach. Quantitative research is based on the philosophy of positivism which emphasizes objective phenomena that are studied quantitatively or carried out using numbers, statistical processing, structures, and controlled experiments (Nana, 2010). The type of quasi-experimental research is research to see the effect of certain treatments on others under controlled conditions (Sugiyono, 2014). The research design used a posttest control group design. The research design is shown in Table 1.
The population of this research is the seventhgrade students of SMP in Lhokseumawe City in the Academic Year of 2022/2023. The samples taken were students of SMPN Arun, SMPN 1 Lhokseumawe, and SMPN 2 Lhokseumawe. A total of 9 classes. There are three classes for each school, consisting of a control class taught by direct instruction and an experimental class taught by POGI and MFI. The sampling technique was carried out in two stages, the determination of the school sample and then continued with the determination of the class sample school sampling technique was carried out by convenience sampling, and the class sample was taken using a random sampling technique with the class as the subject being randomized. The total sample is 251 students. The data obtained from this study are data in the form of scores of scientific literacy skills and science process skills. The data from this study were analyzed using parametric statistics, namely the independent sample t-test for data with normal distribution, and nonparametric statistics, namely the Mann-Whitney U-Test for data not normally distributed. Data analysis was carried out with the help of the SPSS program.
The hypotheses model on students' scientific literacy skills; 2) there is an effect of the MFI model on students' scientific literacy skills; 3) there is an effect of the POGIL model on students' science process skills; 4) there is an effect of the MFI model on students' science process skills; 5) there are differences in the scientific literacy skills of students who are taught using the POGIL model and students who are taught using the MFI model; 6) there are differences in the scientific literacy skills of students who are taught using the POGIL model and students who are taught using the MFI model.
The research instruments used were test questions to determine students' literacy skills, practicum guide sheets, and observation sheets to determine students' science process skills in practicum implementation. The test questions are in the form of descriptions arranged based on a scientific literacy grid. The questions are structured in a personal, local, and global context.

Result and Discussion
The results of the analysis on the value of scientific literacy and science process skills showed that there was a difference in the average score between the experimental class and the control class. The average value of science literacy ability and science process skills of students in the experimental class is higher than the average value of students' science literacy and science process skills in the control class. In the class with the POGIL model, the average value of scientific literacy ability is 32.27, and science process skills are 81.32. In the class with the MFI model, the average value of science literacy skills is 26.84, and science process skills are 88.71. While the control class has an average value of scientific literacy ability of 12.77 and science process skills of 68.54. The comparison of the average value of scientific literacy skills and science process skills in the experimental class and the control class can be seen in Figure 1.

Influence of the POGIL Model on Students' Science Literacy and Science Process Skills
Prerequisite tests in the form of normality and homogeneity tests were carried out on students' science literacy and science process skills scores before testing the hypothesis. The results of the normality test with the Kolmogorov-Smirnov test on the value of scientific literacy obtained a significance value (Sig.) = 0.009 for the experimental class taught by the POGIL model and (Sig.) = 0.000 for the control class. Because of the value of Sig. (0.009) < (0.05) and Sig. (0.000) < (0.05), it can be concluded that the students' scientific literacy scores are not normally distributed. The results of the normality test of students' process skills can be seen in Table 3. Based on the results of this test, the hypothesis test was carried out using nonparametric statistics by using the Mann-Whitney U-Test. The results of the U-test obtained the Asymp value. Sig. (2-tailed) = 0.000. Because of the value of Asymp. Sig. (0.000) < (0.05) then the research hypothesis is accepted, meaning that there is a difference in students' scientific literacy skills between students who are taught using the POGIL model and students who are taught by direct learning. From these results, it can be concluded that learning with the POGIL model affects students' scientific literacy skills. The results of the Mann-Whitney U-Test test students' scientific literacy scores can be seen in Table 4. The average score of students' scientific literacy skills for classes taught with POGIL is 32.27 and 12.77 is low for the control class. In general, this happens due to several reasons, namely students' reading ability, misconceptions, and non-contextual learning. The big dimensions of scientific literacy in its measurement are the scientific process, science content, and the context of science application (Nofiana & Julianto, 2018). The average score of students' scientific literacy skills in the class taught by the POGIL model was higher than in the control class. The results of this study are in line with research results that state that the use of the POGIL model in teaching and learning activities has a positive influence on students' scientific literacy skills (Devitri & Djamas, 2019;Mellyzar et al., 2022;Nikmah & Ellianawati, 2019;Volz, 2019). Based on the output of Test Statistics, it is known that the Asymp value. Sig. (2tailed) of 0.00 < 0.05, it can be concluded that there is a significant effect of the POGIL model on the scientific literacy of grade 7 students at SMP Lhokseumawe City. The difference in students' scientific literacy abilities where the POGIL model is higher is understandable because POGIL learning is process-oriented and student-centered in active learning that uses cooperative learning and guided inquiry activities to develop knowledge, analysis, problem-solving, metacognition, and individual responsibility compared to learning direct transfer of knowledge from teacher to student.
The results of the normality test for the value of students' science process abilities obtained data that were not normally distributed for both the control class and the experimental class taught with the POGIL model. The results of the normality test can be seen in Table 5. Hypothesis testing was continued by using nonparametric statistics, namely the Mann-Whitney U-Test. U-test results obtained the Asymp value. Sig. (2tailed) = 0.000. Because of the value of Asymp. Sig. (0.000) < (0.05) then the research hypothesis is accepted, meaning that there are differences in students' science process skills between students who are taught using the POGIL model and students who are taught using conventional learning. From these results, it can be concluded that the POGIL model has an effect on students' process skills. This is in line with previous research which states that POGIL can improve science process skills for students (Alatas & Fachrunisa, 2018;Idul & B. Caro, 2022;Zamista & Kaniawati, 2015). The difference in process skills between the experimental class and the control class can be caused because, in the inquiry model learning, learning activities are not only in the form of knowledge transfer from teachers and students. However, students were conditioned to be actively involved in learning activities.

The effect of the MFI Model on Science Literacy and Science Process Skills Students
A normality test was carried out on the value of students' scientific literacy skills who were taught with the MFI model and the control class before testing the hypothesis. The results of the normality test with the Kolmogorov-Smirnov test on the value of scientific literacy obtained a significance value (Sig.) = 0.000 for the experimental class taught by the MFI model and the control class. So it can be said that the data is not normally distributed so the hypothesis testing uses the Mann-Whitney U-Test.
From figure 1, the average score of science literacy skills of class students taught by MFI is still relatively low, namely 26.84. The average score is higher than the control class, which is 12.77 and there are differences in students' scientific literacy skills between students who are taught using the MFI model and students who are taught by direct learning. This study is in line with previous research that the MFI model provides better results on students' scientific literacy skills (Gunawan et al., 2019;Heleri et al., 2019). The results of the Mann-Whitney U-Test test scores of students' scientific literacy skills, because of the Asymp score. Sig. (0.000) < (0.05). From these results, it can be concluded that learning with the MFI model affects students' scientific literacy skills. The results of the Mann-Whitney U-Test test on the value of students' process skills can be seen in Table  7. In addition to students' scientific literacy ability which is influenced by the MFI model, students' scientific processability can also be influenced by the MFI model. From the results of the Mann-Whitney test on the value of science process skills, the Asymp value is obtained. Sig. (0.000) < (0.05). From these results, it can be concluded that learning with the MFI model affects students' science process skills. This research is in line with previous research which states that the MFI-based practicum method can improve students' science process skills. The results of science process skills to assess students' abilities in observing, classifying, communicating, hypothesizing, and applying concepts during practicum, experimental class students applying the MFI model, have a percentage that is higher than the control class (Marta et al., 2018).

Comparison of POGIL and MFI on Science Literacy Ability and Science Process Skills of Students
MFI and POGIL affect students' scientific literacy skills. This is because students experience learning that places students at the center of learning. The problems presented by both POGIL and MFI challenge students to solve. POGIL and MFI according to the characteristics of acid-base and neutral materials. The average score of students' scientific literacy skills taught using the POGIL model is 32.27 and the MFI is 26.84. For the average class ability given the POGIL model is better than the MFI model, this is because through POGIL students are more focused on determining problem-solving that produces new concepts for students, because it is more focused, students will be able to understand the concept better and have no difficulty in solving problems. problemsolving.
While in the MFI model learning, a little freedom is given to students, in the selection of tools and materials for solving problems according to the student worksheets (kwon with LKS) given. However, this freedom still confuses some students of Lhokseumawe City Junior High School. For diligent students, they will take advantage of this freedom enthusiastically and look enthusiastic about conducting electrolysis experiments, but for less diligent children, just stay silent, even show a lazy attitude, or tend to chat with friends. So that some students are still not able to gain mastery of the concept as a whole. This is in line with previous research that the POGIL method gives better results than MFI from student learning outcomes (Budiasa & Nyeneng, 2013), but the difference in learning outcomes of these two models is not very significant. Find out the differences between the POGIL and MFI models on students' scientific literacy skills through the Mann-Whitney U-Test, which can be seen in Table 9. Based on the results of the Mann-Whitney U-Test, the Asymp value was obtained. Sig. (2-tailed) = 0.099. Because of the value of Asymp. Sig. (0.099 > (0.05) then the research hypothesis is rejected, meaning that there is no significant difference in students' scientific literacy skills between students taught with the POGIL model and students taught with the MFI model. This is because these two series of learning emphasize the thinking process critically and analytically to find and decide for themselves the answer to a question in question. In line with previous research which concluded that both models promote investigative strategies and values and attitudes and process skills, for example: observing, collecting and organizing data, identifying and controlling variables, formulating and testing hypotheses, explanations, and concluding (Widyaningsih et al., 2012). In the aspect of science process skills, the value of Asymp Sig. (0.000 < (0.05) means that the research hypothesis is accepted, meaning that there is a significant difference in students' science process skills. taught with the POGI model L and MFI models. The average value of students' science process skills with the MFI model is higher than in the POGIL model.

Conclusion
This study aims to determine how the influence of the POGIL model on students' science process skills and scientific literacy, in addition to knowing how the MFI model influences students' scientific process skills and scientific literacy and the comparison of POGIL and MFI models on science process skills and science literacy for junior high school students. Lhokseumawe City. The average value of students' scientific literacy and science process skills in the experimental class was higher than in the control class. In the class with the POGIL model, the average value of scientific literacy ability is 32.27, and science process skills are 81.32. In the class with the MFI model, the average value of science literacy skills is 26.84, and science process skills are 88.71 While the control class has an average value of scientific literacy ability of 12.77 and science process skills of 68.54. Based on the output of the Test Statistics of scientific literacy data and students' science process skills are known that the Asymp score. Sig. (2-tailed) of 0.00 < 0.05, it can be concluded that there is a significant effect of the POGIL model on students' scientific literacy and science process skills. The results for the MFI class scores of students' scientific literacy skills and science process skills, are because of the Asymp score. Sig. (0.000) < (0.05). From these results, it can be concluded that learning with the MFI model affects students' scientific literacy skills and science process skills. Classes taught with POGIL and MFI models with Asymp grades. Sig. (0,099 > α (0,05) then the research hypothesis is rejected, meaning that there is no significant difference in students' scientific literacy skills between students taught with the POGIL model and students taught with the MFI model. This is because these two series of learning emphasize critical and analytical thinking processes to find and decide for themselves the answer to a problem.


s2fieldsofstudy: Education Medicine

text:
Health Education For Adolescents On The Prevention Of Sexually Transmitted Diseases (STDs)

. Sexually transmitted diseases or STDs for short are diseases caused by unhealthy sexual relations. STDs are usually experienced by teenagers, adults, and the elderly due to deviant behavior such as free sex, anal sex, oral sex, or because of direct contact with sufferers through the genital tract, skin touch, vaginal fluids, sperm fluid and sexual intercourse that is not use condoms and other sexual safety devices. Factors that influence adolescent behavior are personal experience, the influence of others, cultural influences, mass media, educational institutions, religion and emotional factors. Therefore, it is necessary to conduct education and socialization on the prevention of sexually transmitted diseases using lecture methods, question and answer and discussion as well as aids or poster media used. From the results of the education and socialization activities, it is hoped that it can prevent sexually transmitted diseases and increase the generation of healthy living in adolescents at GMIH Eben Haezer Church, Ternate.

INTRODUCTION Adolescents are at a vulnerable age, because in this period they are in a transitional period from childhood to adulthood. A transitional period where there are major and essential changes regarding the maturity of spiritual and physical functions both from the biological, psychological and socio-demographic aspects. Various changes cause teenagers to become interested in the opposite sex and have curiosity. Adolescents' opportunities to become interested in sexual relations develop in a complex and dynamic social environment. Lack of or wrong understanding of sexual problems causes adolescents to be at risk of having unsafe sex such as changing partners, using drugs and not using condoms [1]. Some teenagers tend to do risky things including sexual behavior [2]. The risks often faced by adolescents related to sexuality are unwanted pregnancy, abortion and infection with sexually transmitted diseases [3].Adolescents aged 10-24 years (WHO and UNFPA) need serious attention, because they are still of school age and working age. In addition they will enter reproduction. There are relatively many teenagers in Indonesia, which is around 27.6% of the total population of 237.6 million people, this is a potential for development considering that adolescents are the future successors of the nation [4]. According to the World Health Organization (WHO, 2016), there are more than 1 million people suffering from STDs every day from 20 million new cases of infection per year, half of adolescents aged 15-25 years are infected with STDs every year, 1 in 20 adolescents. The prevalence of PMS in developing countries is much higher than in developed countries. Adolescents aged 15-24 years from all sexually active population as much as 25% but contribute to all STD cases almost 50%.
Based on data from the Indonesian Child Protection Commission (KPAI, 2013) it is known that teenagers aged 14 to 18 years, especially in big cities in Indonesia, have had premarital sex and prove that 62.7% of teenagers lost their virgins while still in junior high school, even 21.2%. Some of them are extreme, namely having had an abortion [5]. Based on the Integrated Survey Report and Behavioral Biology (IBBS) of the Ministry of Health of the Republic of Indonesia, the prevalence of sexually transmitted diseases (STD) in 2011 was 179% for gonorrhea and chlamydia and 44% for syphilis [6]. North Maluku is a province in 2019 which ranks 4th with the presentation of premarital sexual relations among teenagers 4%, after West Papua 10%, Papua 5% and North Sulawesi 5% [7]. In the city of Ternate, North Maluku, 8.06% of boys have premarital sex and 4.17% of girls are under 18 years old [8].Teenagers today need to get the main spotlight, because at this time it is very worrying with the development of global modernization and the depletion of one's morals and faith, especially teenagers, this can be seen from the case above, namely the existence of premarital sex that makes teenagers contract sexually transmitted diseases (STDs) due to lack of understanding of youth. Therefore, it is necessary to make efforts to prevent sexually transmitted diseases in adolescents by educating, outreaching and distributing posters.

II. METHODS
Community service activities are carried out by adjusting the environmental conditions at the GMIH Eben Haezer Church in Ternate. The method used is lecture, question and answer and discussion accompanied by tools or poster media. As the partner target group of this activity, 6 people attended the GMIH Eben Haezer Church of Ternate at the end of the service. Partners or targets are involved in educational activities and socialization of the prevention of sexually transmitted diseases (STDs).  (Utomo, 2006), the results of a behavioral survey conducted by the University of Indonesia found that 2.8% of female high school students and 7% of male high school students showed symptoms PMS symptoms. Several sexually transmitted diseases are found in both men and women, such as gonorrhea, syphilis, herpes, HIV/AIDS. Most of the new cases involve adolescents with an age range of 15-24 years [9].

III. RESULT AND DISCUSSION
Based on the research results of Sefti Rompas et al, (2015) at Fajar Bolaang Mangondow East Vocational School, the average value of knowledge before being given health education was 5.80 and after being given health education, the average value of knowledge increased to 9.14, thus it can be concluded that seen an increase in the average score of adolescent knowledge after the provision of health education [10].

Fig 2.Poster Sharing
According to the author's assumption, one of the ways to increase adolescent knowledge is supported by the use of poster media. The content of the poster is adjusted to the educational and socialization materials delivered with pictures and colors and presents the material points. According to Amisani (2009), explaining that media such as posters are very effective in increasing the effectiveness of education and socialization with lecture, discussion and question-and-answer methods, posters in addition to summarizing the entire material, also present interesting images that make it easier for someone to understand the content of the material [10].

Fig 3.Group photo
According to the Indonesian Ministry of Health (2013), efforts to prevent sexually transmitted diseases in reducing sufferers are carried out by education and promotion, namely counseling through campaigns, mass media and poster distribution. Preventing and treating sexually transmitted diseases can be done with various alternatives such as not visiting prostitution sites, because prostitution places generally contain people who have sexually deviant behavior and are places where various sexually transmitted diseases spread among teenagers today. Uncontrolled promiscuity in the era of globalization, dating is considered by teenagers as a trend and modern culture. Dating can lead to rape, sexual disease and death due to fights with partners. Another prevention is to find a good and conducive social environment which is a place of recommendation for adolescent growth and development. This environment will teach, assist, and shape the character of youth into individuals of integrity, religious, broad-minded, intelligent and friendly. Adults with broad insight, a teenager will receive reproductive health education through counseling guidance so that adolescents will be fortified with comprehensive sexual knowledge (able to know about a healthy sexual life, not to date, to know the various risks of sexually transmitted diseases and how to prevent them).

IV. CONCLUSION
By conducting education and outreach activities for youth at GMIH Eben Haezer Church, Ternate, it is hoped that they can prevent sexually transmitted diseases and improve healthy lives.

V. ACKNOWLEDGMENTS
The author would like to thank the Leader of the GMIH Church Eben Haezer Ternate Pastor Yakobus Tjanu, S.Si. M.Si, Head of Youth Church of GMIH Eben Haezer Ternate Deacon Kostan Paigi, Secretary of Youth Church of GMIH Eben Haezer Ternate Mr. Nestorius Sungi, S.Pd, and all those who have succeeded in education and socialization activities. And more specifically, the author would like to thank Fislian Loberce Lawenaung, a student of the Public Health Study Program, Faculty of Health Sciences, University of Muhammadiyah, North Maluku, for being willing to be assisted in carrying out education and socialization.


Attitudes towards nursing research for students from different regions of Perú, 2020

The attitude towards research emphasizes the ability of pronunciation and understanding of the nursing student when carrying out research work, therefore the objective is to determine the attitude towards the research of nursing students from different regions of Peru. It is a quantitative, descriptive, and cross-sectional study, with a population of 149 nursing students from different regions of Peru, who responded to a survey with sociodemographic data and the scale of attitudes towards research. In the results, it was obtained that the attitude towards the research of nursing students, 95.3% have a moderately positive attitude towards research. In conclusion, teaching with respect to research should be strengthened so that they encourage the student to enhance their investigative capacities.

becomes a priority of nursing teachers (Escamilla, 2014), since they must be encouraged in an interesting way where the student is trained correctly as a person and also as a future nursing professional (Halabi and Hamdan, 2012). But today due to the coronavirus pandemic , the research training in the student has been carried out virtually, so they themselves must adapt to the new education and be able to maintain their attitude towards research in the best way and power develop investigative skills that can improve over time (Ramsay et al., 2020).
In Turkey, determined nursing students' attitude towards research. They showed 13.6% of students had scientific research experience and 47.5% had participated in research meetings and seminars. This study suggests that encouraging students to research improves their skills and attitudes toward research (Ünver et al., 2018).
In Cuba, a study was carried out in 58 nursing students, where in their results, 93.1% of the students consider that research is important to care for the patient, 73.1% consider that it is important to manage nursing services and 74.1% consider to improve the quality of patient care, therefore they concluded that the perception towards research that nursing students have is important because it will contribute to their clinical practices (Martínez et al., 2018).
In Peru, a study was carried out on 80 nursing interns, where it was observed that 58.8% of the interns have a negative attitude towards research, 41.3% have a positive attitude towards research. For this reason, they interpreted that a program of research competencies should be carried out in the nursing intern that allows them to improve and be able to exercise their profession correctly (Gálvez et al., 2019).
The objective of the research is to determine the attitude towards the research of nursing students from different regions of Peru, 2020. This will give us important data that allow us to observe how the attitude of nursing students is in research.

Methodology
In this part, the type and design of the research will be developed, also the population in which the research work will be carried out, the inclusion and exclusion criteria will also be given in detail, and finally the technique and instrument of data collection.

Research type and design
The present study, due to its characteristics, way of collecting data and measuring the variables involved, has a quantitative approach. Regarding the methodological design, it is a non-experimental, descriptive, cross-sectional study (Hernández et al., 2014).

Population
The present research work is made up of 149 nursing students from different universities in the regions of Peru.

Inclusion criteria:
 Nursing students who are studying from the 6 th semester to the 10 th semester.  Nursing students who voluntarily participate in the research work.  Nursing students who have signed the informed consent based on the ethical principles of nursing (justice, equity, charity, and non-maleficence).

Exclusion criteria:
 Nursing students who are studying from the 1 st semester to the 5 th semester.  Students who have not signed the informed consent.

Technique and instrument
The technique that was used is the virtual survey of google form, in which through the data collection instrument Scale of Attitudes towards Research (Escala de Actitudes hacia la Investigación (AECIN) in Spanish) which aims to measure the attitude of nursing students towards research.
For data collection, it was structured in 2 sections, 1) Sociodemographic data sheet as age, sex, marital status, type of family, university, semester of study, department, or province; 2) Scale of Attitudes towards Research that comprises 34 items into 3 dimensions, the affective dimension with 9 items, the cognitive dimension with 12 items and the behavioral dimension with 13 items, with a Likertype scale with 5 response options, "0=strongly disagree," "1=disagree," "2=neither agree nor disagree," "3=agree" and "4=strongly agree," the total score is obtained by adding all their items by inverting items 1,4,5,9,14,19,23,27,28,30 and 34, so their total score varies from 0 to 136, where "0 to 45 is negative attitude," "46 to 91 is moderately positive attitude" and "92 to 136 is a positive attitude," the higher the score corresponds to a positive attitude towards research (Quezada et al., 2019).
A data matrix was used in the study that will be designed in the statistical program IBM SPSS Statistics in its version 26.0, which will allow us a better data processing to carry out the tabulations and figures so that they are later described and interpreted in results and discussions, respectively.

Place and application of the instrument
The virtual survey was carried out to measure the attitude towards the research of nursing students from different regions of Peru, which was carried out in the Universities of Lima, Arequipa, Cajamarca, and Iquitos.
In order to start the data collection process, it coordinated with university students from the Universidad de Ciencias y Humanidades, Universidad Nacional de Cajamarca, Universidad San Martin de Porres, Universidad Nacional de San Agustín de Arequipa y Universidad Nacional de la Amazonía Peruana to participate in the research work voluntarily.
In Fig. 1, it is possible to observe the attitude towards the research of nursing students, where 142 (95.3%) present a moderately positive attitude towards research and 7 (4.7%) a positive attitude towards research.  Table 1 shows the attitude toward research of nursing students according to the university from different regions of Peru, which was determined with Pearson's chi-square test (X 2 ). The level of significance of the test obtained a value of 0.7 (p>0.05) (X 2 = 2,845; d.f = 4), for which there is statistical data that verify the relationship between the attitude towards the research of nursing students and the University. Therefore, we can interpret that nursing students from the Universidad de Ciencias y Humanidades to have a moderately positive attitude towards research 70 (94.6%) and 4 (5.4%) have a positive attitude towards research, from the Universidad San Martín de Porres 18 (90%) have a moderately positive attitude and 2 (10%) have a positive attitude towards research, from the Universidad de la Amazonía Peruana 15 (100%) have a moderately positive attitude towards research, the Universidad Nacional de San Agustín de Arequipa 24 (96%) present a moderately positive attitude and 1 (4%) present a positive attitude towards research and from the Universidad Nacional de Cajamarca 15 (100%) present a moderately positive attitude towards research. Table 2 shows the attitude towards the research of nursing students in relation to the semester of study, which was determined with Pearson's chisquare test (X 2 ). The level of significance of the test obtained a value of 1.17 (p> 0.05) (X 2 = 0.116; df = 2), for which there is statistical data that verify the relationship between the attitude towards the research of nursing students and the University.  In Fig. 2, we can observe in the affective dimension of the attitude towards research that 26 (17.4%) present a high attitude, 108 (72.5%) of the nursing students present a medium attitude and 15 (10.1%) have a low attitude towards research.
In Fig. 3, we can observe in the cognitive dimension of the attitude towards research that 94 (63.1%) present a medium attitude, 29 (19.5) present a high attitude and 26 (17.4%) present a low attitude towards research.
In Fig. 4, we can observe in the behavioral dimension of attitude towards research that 88 (59%) of nursing students present a medium attitude, 32 (21.5%) present a high attitude, and 29 (19.5%) %) have a low attitude towards research.
These results are important since they will show us the attitude presented by nursing students from different regions of Peru and the comparison between students at different universities to observe which of all of them presents a better attitude towards research.

Discussions
In this research work, an approach was provided from the perspective of nursing students from different regions of Peru Attitude towards Research.
In the results of the attitude towards research in nursing students, we can observe that nursing students present a moderately positive attitude. This shows that nursing students as they advance in their university stage the level of knowledge towards research is increasing. Gálvez et al. (2019) showed that research knowledge in nursing students gradually increases with the semesters, because in the lower semesters, lack of understanding of statistical programs and limited skills to search for information, there is a negative attitude towards research. In the same way, Ochoa-Vigo et al. (2016) explained that research training in nursing students must be interactive, where the teacher shows their theoretical and practical skills that allow the student the ease of understanding the importance of research so that their research aptitude increases. Likewise, with respect to its dimensions in the affective dimension, it was observed that nursing students have a medium attitude towards research, for this reason, we state that the student at the time of carrying out research must consider the interest and support from the teacher, since that the nursing student places more emphasis on caring and dedication to the patient instead of conducting research. In the same way, Kovačević et al. (2017) affirmed that nursing students are more interested in caring for the patient than doing research work since they mention that the time they had did not allow them to carry out nursing research work.
In its cognitive dimension, nursing students presented a medium attitude towards research, therefore it is interpreted that in the academic field of students, research training is improving every time because the learning they carry is increasingly realized Critical and reflective for the students since their research work will be oriented towards the care of the patient's life, where cognitive and affective aspects will be required that allow them to use care in a more technical and humanized way, Uysal et al. (2017) maintained that the training education of students in the research aspect is very important because it will allow them to develop or improve their technical skills in relation to patient care.
In the behavioral dimension, nursing students presented a medium attitude towards research, this is because the way students understand research is improving throughout their careers and education of teachers is essential for their training and, it will allow them to improve their decision-making in the clinical aspect of patient care. Al Furaikh et al. (2017) argued that the education carried out by teachers should encourage students since it allows them to improve in the clinical aspect, where they show their abilities to solve problems, maintain creative thinking and logical reasoning in themselves to improve health care.

Conclusion
It is concluded that universities must implement educational strategies on research that allow students to show their capacities and knowledge of health towards research. At the same time, teaching with respect to research should be strengthened so that they encourage the student to enhance their research capacities.
It is concluded that students must participate in research conferences related to their career that allows them to show their capacity and development in the area so that universities encourage and develop strategies within the academic plans of nursing students since the Research subjects form a suitable idea for the integral formation of teachers towards students.

Conflict of interest
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


Using social media advertisement data to monitor the gender gap in STEM: opportunities and challenges

Boosting the number of women and girls entering careers involving STEM (Science, Technology, Engineering and Maths) is crucial to achieving gender equality, one of the UN Sustainable Development Goals. Girls and women tend to gravitate away from STEM fields at multiple stages from childhood through mid-career. The leaky pipeline is a metaphor often used to describe the loss of women in STEM and arguably other fields before reaching senior roles. Do interests expressed on social media mirror the leaky pipeline phenomenon? In this article, we collected advertisement data (reach estimates) from Facebook and Instagram disaggregated by US metros, age, gender, and interests related to STEM. We computed the Gender Gap Index (GGI) for each US metro and age group. We found that on Instagram, the GGIs for interest in Science decrease as users’ age increases, suggesting that relatively there is evidence that that women, compared to men, are losing interest in STEM at older ages. In particular, we find that on Instagram, there are plausible relative trends but implausible absolute levels. Nevertheless, is this enough to conclude that online data available from Instagram mirror the leaky pipeline phenomenon? To scrutinize this, we compared the GGIs for an interest in Science with the GGIs for placebo interests unrelated to STEM. We found that the GGIs for placebo interests follow similar age patterns as the GGIs for the interest in Science across US metros. Second, we attempted to control for the time spent on the platform by computing a usage intensity gender ratio based on the difference between daily and monthly active users. This analysis showed that the usage intensity gender ratio is higher among teenagers (13–17 years) than other older age groups, suggesting that teenage girls are more engaged on the platform that teenage boys. We hypothesize that usage intensity differences, rather than inherent interest changes, might create the illusion of a leaky pipeline. Despite the previously demonstrated value and huge potential of social media advertisement data to study social phenomena, we conclude that there is little evidence that this novel data source can measure the decline in interest in STEM for young women in the USA.

INTRODUCTION
Gender equality in Science, Technology, Engineering, and Mathematics (STEM) is vital for the achievement of the internationally agreed development goals, including the 2030 Sustainable Development Goals (SDG). Over the past decades, the global community has made concrete effort in inspiring and engaging women and girls in science. Yet women and girls continue to be excluded from participating fully in science (United Nations, 2022). On average, only around 30% of the world's researchers are women. Moreover, less than a third of female students choose to study higher education courses in subjects like math and engineering (Wood, 2021).
The under-representation of women in STEM fields can be explained by multiple theories, including the "leaky pipeline" metaphor. The leaky pipeline, proposed in 1983, refers to the process by which initially in childhood, people of different genders show similar proportions of interest in the STEM sector. However, gender stereotypes and biases are internalized as they grow up and reach adolescence. This leads to the loss of women in STEM and arguably other fields before reaching senior roles (Berryman, 1983).
Modeling the leaky pipeline remains a challenge to apply nationally or globally. Worldwide surveys and censuses are proportionately expensive and time-consuming . Towards filling this gap, our article presents a different methodology to mirror the leaky pipeline phenomenon by examining the potential of using online sources of data, particularly data provided by the Facebook and Instagram Advertising Platforms.
Concretely, Facebook and Instagram provide their advertisers access to the number of users through their targeted advertising platform. Before an ad is launched, and before any cost is incurred, advertisers are provided with an estimate of how many users are likely to match specific criteria, which can include age, gender, sets of interests, specific locations, and much more. A recent work (Vieira & Vasconcelos, 2021) used Facebook Advertising data to assess the gender gap in STEM in Brazil. Building on this work, we focus on using Advertising data for analyzing the gender gap in STEM across age groups in the USA. The USA is an interesting case study because the majority of Americans say they use Facebook, while the use of Instagram is especially common among adults under 30 (https://www.pewresearch.org/internet/2021/04/07/social-media-use-in-2021/). The USA is also an interesting scenario because although women make up nearly half of the U.S. workforce, they are only 27% of STEM workers (https://www.census.gov/library/ stories/2021/01/women-making-gains-in-stem-occupations-but-still-underrepresented. html).
In this article, we investigate the feasibility of using Facebook and Instagram advertising audience estimates for modeling the leaky pipeline phenomenon in the USA. In particular, we filter the advertising audience estimates by location, age, gender, and interests related to STEM. Then, we compute the gender gap index and compare it across age groups at the US metro level. We perform the analysis at the US metro level because a country such as the USA is very diverse and has different cultural norms in different geographical areas. Some of the metros are more conservative, while others are more liberal. Hence, looking at the USA national level might hide regional variation in gender gaps. Unlike previous work, we perform validity checks in the form of estimates of "placebo" interests and usage intensity gender ratio analysis on Instagram. To the best of our knowledge, Instagram ads audience estimates have not been rigorously tested for data quality and face validity. As such, we demonstrate the dangers of using social media advertisement data as a "black box", and provide methodological tools for future research of such social media data novel source.

RELATED WORK Gender gap in STEM
Reducing the gender gap in Science, Technology, Engineering, and Mathematics (STEM) is essential to achieve the sustainable developing goals, including Goal 5 on Gender equality. According to the United Nations, women are still excluded from participating fully despite the efforts that have been made to inspire women to study and work in STEM (Wood, 2021). Overall, women make up small percentages of STEM graduate students, tenure-track faculty, and tenured faculty (Ceci et al., 2014). Women still pursue STEM degrees at much lower rates than their male peers despite the reversal of the gender gap in educational attainment and near gender parity in math performance (Legewie & DiPrete, 2014). In most countries, a smaller percentage of women than men graduate with a degree in STEM (Card & Payne, 2021). In addition, women worldwide compromise less than 30% of STEM researchers (United Nations, 2022). Girls are less likely than boys to aspire to STEM occupations, even when they have comparable abilities and selfevaluations in math and science (Mann & DiPrete, 2016).
Research shows that women tend to gravitate away from STEM careers due to a relative abundance (i.e., surplus) of interests that make non-STEM careers equally or more appealing (Cardador, Damian & Wiegand, 2021;Wang & Degol, 2017). Along these lines, several studies show that women are more interested in jobs involving people and social interactions such as life sciences or health-related professions. In contrast, men are more interested in jobs involving physical objects and abstract concepts such as physics, mathematics, or engineering (Legewie & DiPrete, 2014;Ceci et al., 2014;Han, 2016;Wang & Degol, 2017;Delaney & Devereux, 2019). As such, for the past three decades, the percentage of bachelor's degrees earned by women in biological sciences and chemistry has increased. However, the percentage of bachelor's degrees earned by women in physics and engineering has stayed stagnant, and in other fields such as computer science, the percentage of degrees earned by women has decreased (Wang & Degol, 2017;Cowgill et al., 2021).
There are many reasons for the lower representation of women in STEM. Gender stereotypes, gender inegalitarian attitudes or discrimination against women are the main reasons that push women away from STEM fields (Soylu & Adams, 2020). Moreover, a survey of college students suggests that lack of interest, feeling unwelcome, and having concerns about the coursework difficulty are reasons that women differentially avoid STEM fields (Carrell, Page & West, 2010). Other research finds that lower performance of girls compared with boys in math and science at school, lack of role models (e.g., female teachers/professors in math and science) and self-confidence (e.g., the competitive environment associated with STEM careers) are factors for the lower representation of women in STEM careers (Zhou et al., 2017;Gomez, Alvarado & Nisperuza, 2020). Similar research tried to summarise six explanations for women's under-representation in math-intensive STEM fields in the USA. These are (a) cognitive ability, (b) relative cognitive strengths, (c) occupational interests or preferences, (d) lifestyle values or work-family balance preferences, (e) field-specific ability beliefs, and (f) gender-related stereotypes and biases (Wang & Degol, 2017). Other research revealed that the under-representation of certain groups in STEM fields is not only a consequence of lower levels of academic qualifications, but also a consequence of lower levels of interest and inclination toward such fields (Riegle-Crumb & King, 2010). The research found that interest in STEM is indeed an essential factor to consider as boys often express higher self-efficacy, more joy in science, and a broader interest in science than do girls (Stoet & Geary, 2018).
The under-representation of women in science, technology, engineering, and mathematics (STEM) fields can be explained by several theories, including the "leaky pipeline" metaphor. According to this metaphor, women are more likely than men to leave STEM fields at multiple time points from the beginning of college through academic tenure (Miller & Wai, 2015). In childhood, girls and boys tend to show similar proportions of interest in the STEM sector. However, this changes as people reach adolescence as gender stereotypes play a role in decreasing the number of women who are interested in STEM. This decrease happens gradually until it reaches tertiary and doctorate levels of education (Stoet & Geary, 2018). In other words, the under-representation of women in STEM, especially in computer science, engineering, and physics begins before college and is attributable to a failure to attract women to enter these fields and stay (Cheryan et al., 2017). In addition, females leave STEM fields at higher rate than do men even when females perform as well or better than their male peers on STEM-related tests or projects (Hill, Corbett & St. Rose, 2010;Reinking & Martin, 2018). Accordingly, the pipeline model sheds light on the importance of increasing the volume of flow of females from grade school to graduate school and preventing "leakage" down the line at all stages (Xu, 2008).
Several studies have evaluated and monitored the gender gap in STEM worldwide. For example, Verdugo-Castro, García-Holgado & Sánchez-Gómez (2019) reviewed instruments and questionnaires related to the gender gap that exists in the field of education within the STEM sector. The review did not only include questionnaires that dealt with the existing gender gap in the STEM sector but also examined particular matters such as the relationship between the choice of studies to be carried out, the influence of parents, self-confidence in the STEM sector, the gender gap in computer engineering, and stereotypes between men and women in adolescence. Focusing on projects funded by the European Union,  analysed several research projects about the gender gap in STEM in different calls across the last 5 years. The analysis acknowledged that most of the projects are focused on STEM or STEM subjects, STEM careers, STEM jobs or entrepreneurship, and different technologies such as robotics or IoT. In addition, Verdugo-Castro et al. (2020) designed a questionnaire to study gender stereotypes about STEM studies among university students. The study revealed that elements that influence students' opinions are not only the contextual, family, and social ones. Factors also include educational experience and the level of education attained, prior interest in the STEM sector, previous vocational training, and branch of study chosen are all factors that shape the student's opinion of higher STEM studies. Verdugo-Castro, García-Holgado & Sánchez-Gómez (2020) conducted interviews with Spanish women working in STEM. Some of the interviewees admitted that they were encouraged to take STEM and try it out, while others admitted that they were afraid to pursue STEM studies. Similarly, García-Holgado, Díaz & García-Peñalvo (2019) interviewed women as a part of the W-STEM project, which seeks to improve strategies and mechanisms for attracting, accessing, and guiding women in Latin America in STEM higher education programs. These interviews were recorded and shared via different channels to make the profiles of these STEM role models visible.
Researchers have offered guidelines and suggestions to attract women to study and work in STEM fields. In particular, Tobar Subía Contento & Nohemi Gamez Aparicio (2020) recommended holding attraction campaigns where the female role models in STEM careers are highlighted, as well as providing data on the number of women studying STEM. Furthermore, the study recommended organizing talks for students to promote vocations in STEM areas. Other studies such as Bennaceur et al. (2018) proposed creating programs and science festivals that encourage girls to study engineering from an early age. The study also proposed developing mentoring programs and promoting the visibility of women as role models through diverse social media channels such as YouTube, Facebook, and Twitter. González-González & García-Holgado (2021) agreed that it is essential to start STEM programs with students at a young age and show them that girls and boys can study and work in STEM. To promote role models in the domain of computer science and software engineering, Ribaupierre et al. (2018) advocated in favour of having a good ratio of women and men on the university teaching team and in favour of good ratio of women and men by software engineering and computer science sub-area. Other work such as Torres-Ramos et al. (2020) stressed the importance of "self-efficacy," known as the ability to accomplish a given task to attract more women and girls in STEM fields. Encouraging and supporting girls' self-efficacy can be achieved by "academic advising, faculty mentorship, tutoring, internship opportunities, and career and skill development" (ibid.). After finding that girls reported significantly lower levels of technical self-efficacy and lower interest in computer science than boys, Brauner et al. (2018) offered several guidelines to adjust children's mental model, including inviting guests from STEM disciplines into the classrooms, organizing Girls' Days, inviting boys and girls into universities for research internships and launching events that help boys and girls understand computing principles and increase their technical self-efficacy.
Quantitative and qualitative techniques have been implemented previously to address the gender gap in STEM. For example, a survey has been used to explore patterns of individuals talented in STEM and determine whether these patterns and experiences differed for men and women or women from different age groups (Heilbronner, 2013). Furthermore, a survey research method was followed to assess the situation of women in STEM in Latin America and Europe and observe if there is any significant gender gap . Focusing on primary school students, a questionnaire has been designed to study if there are gender differences in aspects related to the experience with mathematics (Ayuso et al., 2021). Focusing on undergraduate students, a survey has been distributed to look at the preponderance of female and male students in science-oriented majors leading to STEM careers connected to internal and institutional factors (Faitar & Faitar, 2013). In addition, a survey was carried out to understand the perception of students/teachers and analyze the factors of the gender gap in STEM (Moreno et al., 2014). Other studies relied on existing survey data to study topics related to the gender gap in STEM. For instance, the Freshman Survey data collected by the Cooperative Institutional Research Program (CIRP) was used to analyse how the characteristics of women planning to majors vary across different STEM subfields (Sax & Newhouse, 2018). The Engineers Statistical Data System (SESTAT) was also used to assess the factors influencing the gender gap in persistence in STEM employment in computer science and engineering occupations (Sassler, Michelmore & Smith, 2017). The Educational Longitudinal Surveys of 2002 to 2012 (ELS) was used to assess the gender gap in STEM major completion (Weeden, Gelbgiser & Morgan, 2020), while the 2006 PISA dataset was used to study the gender gap in STEM career expectations across countries (McDaniel, 2016). Other researchers used the NCES (National Center for Education Statistics) longitudinal surveys to consider whether males see their life goals as more compatible with STEM careers than females (Mann & DiPrete, 2013). Other studies applied pre-post surveys to evaluate different kinds of programs that aim to increase females' interest in and enthusiasm for science through (Levine et al., 2015;Levine & DiScenza, 2018). Other researchers conducted interviews with women in STEM professions to capture the low participation of women in STEM professions along with the challenges they face in their working environment (Tandrayen-Ragoobur & Gokulsing, 2021).

Facebook Ads data
Before showing Ads to Facebook users, Facebook allows advertisers to estimate the potential audience estimate, i.e., estimates of how many people an ad could potentially reach, given specific criteria and targeting options. For example, the monthly potential reach estimate for a Facebook-proper audience, not counting Instagram or Messenger, located in the USA, aged 13 to 50+, and female is 67,000,000. The potential reach audience estimate is available for free in the Facebook Ads Manager (https://www.facebook.com/ business/help/200000840044554?id=802745156580214). The estimated audience size can be filtered by age, gender, location, and device type. In addition, the estimated audience size can be filtered by users' interests. The interests filter depends on what Facebook users engage with and like on the platform. Facebook determines a user's interests based on the pages they like, the content they view, and the ads they click (https://www.facebook. com/help/562973647153813).
Facebook's audience estimates have been used for a range of studies connecting society and the world's online population. For example, researchers took advantage of Facebook's audience estimate to predict gender gap in internet use . The monthly active Facebook users have been filtered by age, gender, and country. The study demonstrated that advertising digital data from Facebook can be used to complement traditional data sources to monitor global development indicators linked to digital gender inequality. Similar research worked on filtering Facebook Ads data by gender, geography, age, education, industries, and device type in order to examine variation in gender gaps in India . The study found that there is a significant sub-national variation in gender gaps in Facebook use and higher gender gaps tend to be present in states with greater gender inequality in schooling and lower levels of social and economic development. Other researchers developed a metric called Facebook Gender Divide (FGD) to measure gender differences in Facebook access and activity in 217 countries (Garcia et al., 2018). The FGD tends to be associated with other types of gender inequality, including economic, health, and education inequality. Recently, researchers have looked into using Facebook Ads data to measure the gender gap in mobile device usage duration and saw patterns of gender gaps disfavouring women, especially among younger people (Sabri, Kashyap & Weber, 2021). In a different article, Gil-Clavel & Zagheni (2019) extended the scope and used Facebook Ads data to include the role of age. The authors described that in countries in North America and Northern Europe, patterns of Facebook adoption do not differ significantly between older and younger adults. In Asian countries, which have high levels of gender inequality, differences in levels of Facebook adoption by gender disappeared at older ages (ibid.).
Facebook Ads data has also been leveraged to study culture and assimilation. In a recent study, Facebook data was filtered by geography and interests related to specific cultural traits, providing a collection of massive data on human behaviors that are used to complement traditional cultural metrics (Obradovich et al., 2020). Moreover, Facebook Ads data has been filtered by geography and interest in Brazilian dishes in order to focus on how a foreign culture such as the Brazilian culture is spread around the world (Vieira et al., 2020). In parallel, researchers found that Facebook Ads data can be used to explore the cultural assimilation of Mexican Immigrants by looking at musical preferences. Accordingly, Facebook Ads data has been filtered by interest in musical genres and Facebook's ethnic affinities demographic attributes (Stewart et al., 2019). Also, researchers disclosed that Facebook data can provide insights into the assimilation of Arabic-speaking migrants in Germany. For this, Facebook data has been collected and filtered by interests and by different populations (e.g., non-expats living in Germany, or Arabicspeaking expats living in Germany) (Dubois et al., 2018).
Facebook Ads data has been used to reinforce research in the political science and health science domains. For instance, the research looked into leveraging Facebook Ads data to infer the audience demographics of politicians in the electoral race (Ribeiro, Kansaon & Benevenuto, 2019). Here, Facebook Ads data has been filtered by interests related to candidates to calculate audience demographics. In the context of health, Facebook Ads data has been collected to compute the proportion of users in a target demographic group with interest related to schizophrenia (Saha et al., 2017). This data has been used to construct a plausible index of population-scale schizophrenia awareness. Furthermore, Facebook Ads data has been utilized to track health conditions associated with tobacco use, obesity, and diabetes (Araujo et al., 2017). This research contributes to previous work by performing quality check in the form of comparing the performance of placebo interest. In consideration of that, Facebook Ads data has been collected and filtered by country, interests in health conditions, interest in placebo, age, and gender. Equivalently, previous work introduced the use of placebo interests and compared these interests to interests related to four diverse health conditions: (1) diabetes (type II), (2) obesity, (3) food sensitivities, and (4) alcoholism (Mejova, Weber & Fernandez-Luque, 2018). The study showed that placebo interest such as Reading and Technology display a nontrivial correlation with other health interest such as Diabetes and Obesity. This makes measuring placebo interests a critical step in verifying the significance of health-specific results.
Assessing the gender gap in STEM has been scrutinized using Facebook Ads data. Focusing on Brazil as a case study, Vieira & Vasconcelos (2021) argued that even with a larger proportion of women on Facebook, STEM interests are still concentrated towards men. In particular, Facebook Ads data disclosed that college majors related to Environmental Science, Engineering, and Computer Science have more men interested in them. However, college majors related to Life Science and Math/Physical are preferred by women (ibid.). Additionally, Vieira & Vasconcelos (2021) (ibid.) applied gender balance analysis on demographic subgroups, in particular for education levels and age groups. The analysis confirmed that the gender gap in STEM widens significantly in the transition from Bachelor's to post-graduate levels (e.g., Masters or Doctorate levels) as well as into research careers. The authors also compared Facebook users' age with their college major interests. Explicitly, the authors split the population into four subgroups: adolescent (13-19 years), early adulthood (20-39 years), adulthood (40-64 years), and maturity (65 years or more). The authors observed the same pattern as the educational level where the proportion of female Facebook users interested in each major decreases as older as they are. Over their lifetime, women tend to lose even more interest in STEM majors such as Aviation and Mechanical Engineering.

LinkedIn ads data
Similar to Facebook, LinkedIn allows advertisers to estimate the reach of their campaigns. For instance, in 2017 LinkedIn estimated that an advertisement "targeted to 18-to 24-yearold males in San Francisco Bay Area with knowledge in Java, has the potential to reach 11,000 people" (Haranko et al., 2018). Such estimates of employment and skills are hard to obtain using other traditional data sources. LinkedIn advertisement data has been filtered by skills, industry, age, location, and gender in order to understand gender gaps in employment along various dimensions (ibid.). The study reported that Education and and Medical/Health care seem to be the most female dominant categories, whereas Construction and Manufacturing seems to be the most male dominant categories. The study also reported that there is a high correlation between LinkedIn data and traditional data source provided by the US Bureau of Labor Statistics. Building on this work, LinkedIn data has been disaggregated by job seniority, job function, field of study, industry, age, location, and gender to examine gender inequalities in the professional domain (Kashyap & Verkroost, 2021). This research outlined that gender inequality on LinkedIn is exceptionally high among older individuals and in Africa, the Middle East, and parts of Asia. The study agrees that LinkedIn data is correlated with traditional data sources available from the International Labour Organization's (ILO) Statistical Database. Furthermore, LinkedIn data has been disaggregated by industry, location, and gender to examine variation in the professional gender gap in IT industry (Verkroost et al., 2020).
The authors observed that in the large majority in countries worldwide, there are more men than women in IT. Specifically, there are almost four times more men than women in IT industries like networking and computer hardware, while there are almost twice as many men as women in the telecommunication and internet industries. The authors also observed that LinkedIn data tend to be similar to other traditional data sources available from Eurostat, at least for European countries.
The previous studies discussed above use LinkedIn and Facebook Ads data to monitor digital and employment gender gaps, culture, politics, health, and wellbeing. Our study is instead focused on the combination of Facebook and Instagram data for measuring gender gap in STEM. Different from research that explores the gender gap in STEM in Brazil, this research examines the gender gap in STEM in the USA. Specifically, our study is different from Vieira & Vasconcelos (2021)'s work in that it considers different geography, platforms, and age groups. We aim to focus on the gender gap in STEM among teenagers and young adults. Our work builds on previous research strands by comparing gender gap in advertisement data across age and USA metropolitan area. Additionally, our research introduces measuring placebo interests and usage intensity gender ratio in order to validate findings related to gender gap in STEM. This is an important methodological contribution as it shows that many apparent differences in terms of interest profiles might be explained by differences in time-spent-on-platform.

METHODOLOGY Data collection
Facebook and Instagram data has been retrieved from Facebook's Marketing application programming interface (API). The Web interface of the Ads Manager (https://www. facebook.com/business/tools/ads-manager) can render equivalent data, but using the API makes programmatic access easier (Mejova, Weber & Fernandez-Luque, 2018). Figure 1 shows a screenshot of the Facebook Ads Manager illustrating the potential reach to an audience likely to match targeting criteria. The Facebook marketing API provides two metrics: Daily Active Users (DAUs) and Monthly Active Users (MAUs). On Facebook for developers, DAUs are defined as the "estimated number of people that have been active on your selected platforms and satisfy your targeting spec in the past day" (https://developers. facebook.com/docs/marketing-api/reference/ad-campaign-delivery-estimate/), while MAUs are defined as the "estimated number of people that have been active on your selected platforms and satisfy your targeting spec in the past month" (https://developers. facebook.com/docs/marketing-api/reference/ad-campaign-delivery-estimate/).
The DAUs and MAUs reach estimates have been queried from the API and filtered by location, age, gender, interest, and platform as the following: Location: This covers all the USA metropolitan statistical areas (https://en.wikipedia. org/wiki/Metropolitan\_statistical\_area) (210 metros). We collected geographical data to capture variations in gender gaps in the USA.
Gender: This covers female, male, and all genders.
Interest: We used the Facebook Ads Manager interface to identify the key interests related to STEM. As such, we filtered the reach estimate by four interests: Science, Technology, Engineering, and Mathematics.
Platform: This covers two platforms: Facebook (this includes all platforms such as Facebook, Instagram, Audience Network and Messenger), and Instagram-only.
We included Instagram-only as Instagram is known to be popular with teenagers (https://www.statista.com/statistics/248769/age-distribution-of-worldwide-instagramusers/.). The MAUs metric does not report numbers under 1,000 to prevent the targeting of small groups of individuals. We are including MAUs values under 1,000 in the analysis, but we are replacing these small values with NAs (On Facebook, we got 3,881 NAs out of 12,600 data points. On Instagram, we got 4,966 NAs out of 12,600 data points).
We also collected the number of users not filtered by any interest from Facebook and Instagram, in order to calculate the Gender Gap index.

Gender gap index
We have used the MAUs to calculate the Gender Gap Index (GGI) for each USA metro and age group as the following: GGI ¼ Female to male gender ratio filtered by each STEM interest Female to male gender ratio of the number of users in each platform (1) A GGI less than 1.0 indicates a gender gap with women at a disadvantage. Conversely, a value equal to or larger than 1.0 indicates that gender parity for the interests considered has been achieved or exceeded.

Min and max GGIs
In this section, we report the min (= worst for women) and max (= best for women) GGI values for age groups 13-16 and 26-29 years on Facebook and Instagram (interest in Science). On Facebook, the min GGI for age group 13-16 years belongs to Harlingen-Wslco-Brnsvl-Mca (0.982), while the max GGI for the same age group belongs to Orlando-Daytona Bch-Melbrn (1.516). For age group 26-29 years, the min GGI belongs to Laredo (0.885), while the max GGI for the same age group belongs to Greenwood-Greenville (1.49). On Instagram, the min GGI for age group 13-16 years belongs to Salt Lake City (1.095), while the max GGI for the same age group belongs to Dallas-Fort Worth (1.642). For age group 26-29 years, the min GGI belongs to El Paso-Las Cruces (1), while the max GGI for the same age group belongs to Columbus-Tupelo-West Point-Houston (1.725). Table 1 shows the top and bottom three GGI values for age group 13-16 years old, while Table 2 shows the top and bottom three GGI Values for age group 26-29 years old. We expected to see Metros with low GGI (<1), especially with small Metros that might have higher gender gaps. However, this is not the case as there are many GGI values that are >1 in Facebook as well as Instagram (On Facebook, we obtained 4,092 GGI values that are <1, while we obtained 4,627 GGI values that are ≥1 out of 12,600 data points. On Instagram, we obtained 2,862 GGI values that are <1, while we obtained 4,772 GGI values that are ≥1 out of 12,600 data points. These GGIs include interests related to Science, Technology, Engineering, and Mathematics).

Comparison between GGIs across age groups
Here, we compare the GGI across age groups on each platform. On Facebook, we found that the GGI of interest related to Science fluctuates across age groups (Fig. 2). Each point on the boxplot corresponds to a different USA metro. The GGI decreases from age group 13-16 to age group 15-18, then increases from age group 16-19 to age group 21-24. The GGI decreases again at age group 22-25 and then increases at age group 23-26 until reaching age group 26-29 years. We have conducted two tests: the Kruskal-Wallis and the Paired t-test. The Kruskal-Wallis is conducted in order to  determine if there is a statistically significant difference between the GGIs for different age groups. The paired t-test is conducted in order to determine if there is a difference between the GGIs at age groups 13-16 and 26-29. After conducting Kruskal-Wallis on Facebook data, we reject the null hypothesis and conclude that not all the group medians are equal (Kruskal-Wallis chi-squared = 85.425, df = 13, p-value = 1.041e−12, n = 2,940, effsiz = 0.0248). The paired t-test results designates that there is significant evidence of change in GGIs between age groups 13-16 and 26-29 years old (t = −5.0328, df = 35, pvalue = 1.453e−05, 95% CI [−0.15179885 to −0.06453449], mean of the differences = −0.108, n = 210, effsiz = 0.839). The mean of the difference in GGI between the two age groups is reduced by 0.11 points. Concerning other interests on Facebook, such as interest in Technology, Engineering, and Mathematics, we did not observe a decrease in the GGI as users get older (Fig. A1). On Instagram, however, we noticed that the GGI of interest related to Science is decreasing across age groups, suggesting that women, compared to men, are relatively losing interest in STEM at older ages (Fig. 3). There was a statistically significant differences between GGI in different age groups as assessed using the Kruskal-Wallis test (Kruskal-Wallis chi-squared = 127.47, df = 13, p-value = 6.67368e−21, n = 2,940, effsiz = 0.0391). The carried out Paired t-test reveals that the mean paired difference between between age groups 13-16 and 26-29 years does not equal zero (t = −8.2268, df = 17, pvalue = 2.489e−07, 95% CI [−0.3296102 to −0.1950564], mean of the differences = −0.262, n = 210, effsiz = 1.94). The GGI between the two age groups is reduced by 0.26 points. Concerning interests on Instagram related to Technology, Engineering, and Mathematics, we did not notice a significant decrease in GGI across ages, as shown in (Fig. A2). Overall, the results denote that the GGIs on Instagram for interest in Science only mirror the leaky pipeline as women are losing interest in Science as they grow up. Specifically, (i) the relative trend across the ages is in line with a leaky pipeline, but (ii) the absolute level (actual GGI values remains > 1.0 all the time) is not in line with a leaky pipeline. Before confirming this outcome and trying to understand the trend on the relative level, we collect and analyse data related to placebo interests on Instagram. Figure 3 provides potential evidence for a leaky pipeline on Instagram as the GGI is decreasing for older age groups, indicating a relative loss in Science of women compared to men. However, to rule out certain confounding factors, we also collected data and performed an analysis for a set of placebo interests. Placebo interests are interests on Instagram that should not have an obvious causal link with interests in STEM but that might still turn out to be correlated due to latent factors such as general engagement on the platform (Mejova, Weber & Fernandez-Luque, 2018). Placebo interests are helpful to understand if we are really measuring interest in science on Instagram and not the level of activity on the platform or other unknown latent factors. Intuitively, "these interests are meant as a placebo wherein no topic-specific treatment is performed, and any effect observed is due to the random or causal factors outside the topic" (ibid.). Similar to previous work (ibid.), we used popular generic interests that are not a priori linked to the four STEM interest studied on Instagram. The generic interests include Facebook, Reading, Music, and Entertainment. We also added other gendered interests such as Cars and Beauty. As many of these Placebo interests are very general with audiences of millions of users worldwide, they can be seen as a kind of proxy for general engagement on the platform: if a user really shows no sign of interest in, say, any type of music, then that could be plausible due to a lack of engagement in activities on the platform. We collected the reach estimates from the API filtered by location, age, gender, and placebo interest as the following:
Gender: This covers female, male, and all genders. Interest: We used the Facebook Ads Manager interface to exhaustively enumerate Placebo interests on Instagram. Then, we filtered the reach estimates by interest in Facebook, Reading, Music, Entertainment, Cars and, Beauty.
The MAUs have been used to calculate the gender gap index (GGI) for each USA Metro and age group as the following: Placebo GGI ¼ Female to male gender ratio filtered by each placebo interest Female to male gender ratio of the number of users in each platform We discern that the placebo GGIs for interest related to Facebook and Reading increase as users get older, demonstrating that women, compared to men, are not losing interest in Facebook and Reading at older ages (Fig. 4). Nevertheless, we discern the placebo GGIs for interest related to Music, Entertainment, Cars and, Beauty decrease as users get older, showing that women, compared to men, are losing interest in Music, Entertainment, Cars and Beauty at older ages (Fig. 5). This makes the relative GGIs patterns that we observed on Instagram for interest in Science not unique as placebo interest follow similar patterns across age groups. For example, Fig. A3 shows that the GGIs for interest in Science for different age groups on Instagram have a similar pattern as the GGIs for interest in Music. In fact, we also observed that the 26-29 age group GGI for interest in Science is correlated with the GGI for interest in Facebook (r = 0.46), interest in Reading (r = 0.41), and interest in Music (r = 0.40). In addition, we observed that the 13-29 age group GGI for interest in Science is correlated with the GGI for interest in Facebook (r = 0.33), interest in Reading (r = 0.41), and interest in Music (r = 0.23). Within placebo interests, we observed that the 13-29 age group GGI for interest in Facebook is correlated with the GGI for interest in Reading (r = 0.66), interest in Entertainment (r = 0.63), and interest in Music (r = 0.63). In general, as shown in Fig. A4, the GGIs for interest in STEM (with the exception of interest in Math) are correlated with the GGIs for interest in placebo. As such, we cannot conclude that online data provided by Instagram reflect the leaky pipeline phenomenon when we measure GGI for interest on Science.

Activity level on the platform
In the previous section, we found that the GGIs for age groups 13-16 and 14-17 years are higher than the GGIs for the other older age groups when we consider interests in In other words, DAUs to MAUs ratio serves as a proxy for the Usage Intensity Gender Ratio. Equation (3) captures female usage intensity divided by male usage intensity. For user groups who spend more time on the platform, we would expect a bigger proportion of the monthly active users (MAUs) also to be daily active users (DAUs). Correspondingly, we expect a higher DAUs to MAUs ratio and thus higher usage intensity gender ratio. As indicated in Fig. 6, age groups 13-16 and 14-17 years have higher DAUs to MAUs ratios than the other age groups thus, girls who are 13-17 years old are more likely to spend more time on the platform. The more time that users spent on the platform, the more Instagram or Facebook assigns interest for users. For example, if we have two users (User A and User B) within the same age group, but User A spends more time on Instagram than User B. Then, Instagram is more likely to assign more interests to user A. These assigned interests are possibly false positive and do not accurately represent users' actual interests. Facebook and Instagram use machine learning algorithms to infer interests about its users (https://www.facebook.com/business/news/goodquestions-real-answers-how-does-facebook-use-machine-learning-to-deliver-ads). Users who spend more time on the platform are also more expected to engage more on the platforms (i.e., like and comment on posts). The more interactions on the platform, the more likely the algorithm is run again and again to assign interest to users. For instance, users who are 13 years old are more engaged on the platform, so Instagram picks more interest for them related to both STEM and Placebo. These teen users are expected to be targeted based on interests that do not reflect their actual interests. This is why we saw that for interests in Science and interests in placebo, the GGIs for age groups 13-16 and 14-17 years are higher than the GGIs for the other older age groups. In all likelihood, this stipulates what we are measuring on Instagram is time spent on the platform and usage intensity and, not actually interest related to STEM.

Science hashtag on instagram
In order to explore what is classified as Science on Instagram, we also looked at pictures under the hashtag #science. Figure 7 shows that there is a variety of things covered under the Science hashtag on Instagram. These things could be directly related to science (e.g., pictures illustrating the principles of hydro-power) and unrelated to science (e.g., images of a door and roof). This raises many questions about how Instagram defines Science and how it assigns interests related to science to users. This also suggests that what we are measuring on Instagram is not necessarily related to STEM or the leaky pipeline. We also inspected pictures under the hashtags #technology, #engineering, and #math on Instagram (Fig. A5) to understand what might be classified as being related to an interest in STEM. As with #science, we found that pictures under those hashtags often do not appear to be related to STEM.

DISCUSSION
This article explores the use of Facebook and Instagram Ads audience estimates for modeling the gender-specific decline in interest in STEM across age, i.e., modeling one element of the leaky pipeline phenomenon. At the USA Metro level on Facebook, women, compared to men, are not losing interest in either Science, Technology, Engineering, and Math at older ages. On Instagram, however, the relative trend indicates that women, compared to men, are losing interest in Science at older ages. While this relative trend across the ages is in line with a leaky pipeline, the absolute levels observed are not: even at older ages, the data suggests higher levels of interest for women than for men. In order to understand this more, we assess the quality of Instagram data by introducing placebo baselines, and usage intensity gender ratio analysis. Among our findings, we show that interests in Placebo such as interest in Music display similar patterns as interest in Science across age groups on Instagram. Moreover, we show that teenagers (13-17) years old have higher usage intensity gender ratio than older users. As teens spend more time on the platform, this makes them vulnerable to being assigned more interests that are attached to their profiles. This could suggest that we are using the marketing APIs as a tool to measure activity level on the platform, and not interest in STEM. We also looked at pictures under the hashtag #science on Instagram. Despite that the hashtag covers a wide range of things that could be classified under Science, there are pictures that are not entirely related to Science or STEM in general. This indicates that it is hard to obtain a definition or classification of Science from Instagram. While it is known that, at a high level, Facebook and Instagram assign interests based on users' publications and activities on the platform (https://www.linkedin.com/pulse/how-facebook-defines-intereststargeting-irina-yandulina), the details of the Instagram interest classification algorithm remain a black box. Facebook and Instagram do not only track users on their platform, but also track web behavior outside of their own platform. This includes tracking users across other websites and services, into the various apps they are using on their phone and to the places they physically visit in the real world-especially if they decide to check in on Facebook while they are there (https://www.wired.com/story/ways-facebook-tracks-youlimit-it/). For example, Aguiar et al. (2022) showed that Facebook can track 55% of the websites visited by Facebook users, and 44% of non-Facebook users, which amounts to 41% and 38% of browsing time, respectively. Therefore, profiling users is far beyond the science hashtag example given in the article. Recently, Apple released an update for iPhones with a new popup that asked users if they wanted to allow apps on their phones to target the user for ads (https://www.cnbc.com/2021/11/13/apples-privacy-changes-showthe-power-it-holds-over-other-industries.html). This change in Apple's privacy could lead to the suffering of the Facebook Ads targeting. More important, similar patterns that are observed between interests in Science and interest in Placebo or usage intensity warrant extra caution in the use of Marketing APIs as a source of social interest. Facebook advertisement data that are filtered by interests have shown promise in measuring schizophrenia awareness (Saha et al., 2017), investigating culture at the country, subnational, and local levels (Obradovich et al., 2020), assessing how the Brazilian culture is distributed around the world (Vieira et al., 2020), and exploring the cultural assimilation of Mexican Immigrants (Stewart et al., 2019). In spite of that, this study shows that there is no strong evidence to conclude that online advertisement data that are filtered by interests can be used to study the gender gap in STEM in the USA. These interests in STEM do not reflect users' actual interests and are more prone to be false positives. This study contributes to previous work by not only combining Facebook and Instagram advertisement data to look at the gender gap in STEM in the USA, but also evaluating these novel online sources and comparing them to placebo and usage intensity gender ratios.
Still, it may be too early to give up on this potentially rich data source. Our effort is restricted to specific interests in STEM; we acknowledge that it is impossible to generalize the limitations we have found without future studies. Future work, for example, could look at partitioning the data based on the size of the US metro and comparing the gender gap index across age groups accordingly. Furthermore, to assess the accuracy of interest assignment, one could use the advertising platform to run a survey assessing the interests of the reached audience directly. The DAUs and MAUs change over time, with DAUs changing several times within 24 h and MAUs typically changing every 1-2 weeks. This is both a strength, in terms of recency, as well as a limitation, in terms of time-dependency of the analysis. Furthermore, the algorithms used to infer user attributes can occasionally change, which can make it hard to obtain consistent and comparable signals over time. For example, (Palotti et al., 2020) observed a sudden change in March 2019 in how Facebook classifies users by countries they have lived in. Unfortunately, Facebook does not provide access to historical data to show changes over time. Therefore, long-term effort is required to monitor the data with respect to seasonal variation across, say, 1 year. Finally, replicating our analysis using placebo interests in other countries could help with understanding whether interest-specific, rather than usage-related, conclusions can be drawn in other contexts.
We hope that this work will encourage future efforts to use our methodology to gather user interest from Facebook and Instagram for other use cases and research problems. We also hope that the results presented here will encourage future researchers to test the face validity of these potentially rich data sources.