Osot Nerapusee, Kumpanart Soontornvipart, Thanakrit Pettong, Nutkritta Phongsuchat, Doungkamol Lunsucheep, Chanthawat Patikorn, Sornkanok Vimolmangkang, Puree Anantachoti
Thai veterinarians' perceptions of cannabidiol products for dogs with osteoarthritis: a qualitative interview study
Frontiers in veterinary science
improving understandability health information significantly increase costeffectiveness efficiency health education programs vulnerable populations pressing need develop clinically informed computerized tools enable rapid reliable assessment linguistic understandability specialized health medical education resources paper fills critical gap current patientoriented health resource development requires reliable accurate evaluation instruments increase efficiency costeffectiveness health education resource evaluation aimed translate internationally endorsed clinical guidelines machine learning algorithms facilitate evaluation understandability health resources international students australian universities based international patient health resource assessment guidelines developed machine learning algorithms predict linguistic understandability health texts australian college students aged 2530 years nonenglish speaking backgrounds compared extreme gradient boosting random forest neural networks c50 decision tree automated health information understandability evaluation 5 machine learning models achieved statistically better results compared baseline logistic regression model also evaluated impact linguistic feature performance 5 models found information evidentness relevance educational purposes logical sequence consistently important numeracy skills medical knowledge assessing linguistic understandability health education resources international tertiary students adequate english skills international english language testing system mean score 65 high health literacy mean 165 short assessment health literacyenglish test results challenge traditional views lack medical knowledge numerical skills constituted barriers understanding health educational materials machine learning algorithms developed predict health information understandability international college students aged 2530 years thirteen natural language features 5 evaluation dimensions identified compared terms impact performance models health information understandability varies according demographic profiles target readers international tertiary students improving health information evidentness relevance logic critical