xml: <clinical_study>
    <!-- This xml conforms to an XML Schema at:
      https://clinicaltrials.gov/ct2/html/images/info/public.xsd -->
    <required_header>
      <download_date>ClinicalTrials.gov processed this data on September 20, 2023</download_date>
      <link_text>Link to the current ClinicalTrials.gov record.</link_text>
      <url>https://clinicaltrials.gov/ct2/show/NCT05995574</url>
    </required_header>
    <id_info>
      <org_study_id>STU-2023-0462</org_study_id>
      <nct_id>NCT05995574</nct_id>
    </id_info>
    <brief_title>Adolescent Contraception: Communication Training Intervention to Engage Pediatrician-Adolescent-Mother Triads</brief_title>
    <official_title>Adolescent Contraception: Communication Training Intervention to Engage Pediatrician-Adolescent-Mother Triads</official_title>
    <sponsors>
      <lead_sponsor>
        <agency>University of Texas Southwestern Medical Center</agency>
        <agency_class>Other</agency_class>
      </lead_sponsor>
    </sponsors>
    <source>University of Texas Southwestern Medical Center</source>
    <oversight_info>
      <has_dmc>No</has_dmc>
      <is_fda_regulated_drug>No</is_fda_regulated_drug>
      <is_fda_regulated_device>No</is_fda_regulated_device>
    </oversight_info>
    <brief_summary>
      <textblock>
        In order to support the desire of most adolescents to delay pregnancy (parenting) until their&#xD;
        own adulthood, pediatricians must be comfortable and skilled in having reproductive health&#xD;
        conversations with adolescents and the mothers of adolescents. Artificial intelligence, such&#xD;
        as a chatbot, could be programmed to simulate the perspective of the mother or the daughter&#xD;
        as a tool for pediatricians to practice communication before interacting with &quot;real&quot;&#xD;
        families. Through human-centered design, an iterative problem-solving approach, our overall&#xD;
        goal is to develop and test a communication training chatbot tool that is accurate,&#xD;
        developmentally tailored for adolescents and mothers, culturally tailored, and aids&#xD;
        pediatricians to manage resistance and conflict about contraception to ultimately close&#xD;
        disparities in teen births. The investigator's primary hypothesis is that a communication&#xD;
        focused intervention will improve pediatrician interactions with dyads about contraception.&#xD;
        Through three stages, the study team will develop an artificial intelligence tool as a&#xD;
        behavioral intervention. The study team will gather basic communication data to generate,&#xD;
        refine, modify, adapt, and pilot test novel communication tools with real-world encounters&#xD;
        while keeping humans at the center of attention&#xD;
      </textblock>
    </brief_summary>
    <detailed_description>
      <textblock>
        In Stage 1 (formative), the investigators will characterize communication content and styles&#xD;
        of pediatricians (residents, fellows, attendings) and adolescent-mother dyads on&#xD;
        contraception discussions with 50 real-life clinical encounters. Through separate&#xD;
        post-encounter surveys of each pediatrician-adolescent-parent &quot;triad&quot; using validated scales,&#xD;
        the investigators will describe the overall quality of the interactions (concordant&#xD;
        contraception plans, satisfaction), communication (confidence, comfort, self-efficacy,&#xD;
        confidentiality) and decision-making (perceived autonomy, ownership, and power) to determine&#xD;
        &quot;ideal, moderate and poor&quot; communication interactions. Then, separate group interviews (n =&#xD;
        15, with adolescents, mothers and physicians) will review recorded vignettes to identify&#xD;
        optimal culturally tailored communication strategies. In Stage 2 (development), the&#xD;
        investigators will upload de-identified and anonymized vignettes to an AI communication&#xD;
        training tool to develop and refine the tool through prompt engineering and performance&#xD;
        evaluation with adolescent, parent and pediatrician advisory boards. Then, the investigators&#xD;
        will iteratively test the acceptability and feasibility of the tool on 10 pediatrician&#xD;
        learners. In Stage 3 (proof-of-concept), the investigators will test how an AI communication&#xD;
        intervention tool can improve reproductive health encounters by comparing evaluation results&#xD;
        of 50 pre-training and 50 post-training real encounters. This study will advance pediatrician&#xD;
        communication training, empower adolescent sexual health, support mothers' priorities, and&#xD;
        test how AI communication training chatbots can improve triadic interactions.&#xD;
      </textblock>
    </detailed_description>
    <overall_status>Not yet recruiting</overall_status>
    <start_date type="Anticipated">April 1, 2024</start_date>
    <completion_date type="Anticipated">March 31, 2029</completion_date>
    <primary_completion_date type="Anticipated">March 31, 2029</primary_completion_date>
    <phase>N/A</phase>
    <study_type>Interventional</study_type>
    <has_expanded_access>No</has_expanded_access>
    <study_design_info>
      <allocation>N/A</allocation>
      <intervention_model>Single Group Assignment</intervention_model>
      <intervention_model_description>single intervention arm with pre- and post- evaluation.</intervention_model_description>
      <primary_purpose>Health Services Research</primary_purpose>
      <masking>None (Open Label)</masking>
    </study_design_info>
    <primary_outcome>
      <measure>Questionnaire</measure>
      <time_frame>Pre-training, baseline.</time_frame>
      <description>Multiple questions will be aggregated to arrive at one reported value to determine &quot;ideal, moderate and poor&quot; communication interactions.</description>
    </primary_outcome>
    <primary_outcome>
      <measure>Questionnaire</measure>
      <time_frame>Post-training, through study completion, an average of 1 year.</time_frame>
      <description>Multiple questions will be aggregated to arrive at one reported value to determine &quot;ideal, moderate and poor&quot; communication interactions after training.</description>
    </primary_outcome>
    <secondary_outcome>
      <measure>Acceptability Questionnaire</measure>
      <time_frame>Through study completion, an average of 1 year.</time_frame>
      <description>Weiner's implementation outcome measure Acceptability of Intervention Measure (AIM). Items are measured on a 5-point Likert scale (Completely Disagree-Completely Agree). Score is calculated mean with the higher score meaning acceptability.</description>
    </secondary_outcome>
    <secondary_outcome>
      <measure>Feasibility Questionnaire</measure>
      <time_frame>Through study completion, an average of 1 year.</time_frame>
      <description>Weiner's implementation outcome measure Feasibility of Intervention Measure (FIM). Items are measured on a 5-point Likert scale (Completely Disagree-Completely Agree). Score is calculated mean with the higher score meaning feasibility.</description>
    </secondary_outcome>
    <number_of_arms>1</number_of_arms>
    <enrollment type="Anticipated">465</enrollment>
    <condition>Communication</condition>
    <arm_group>
      <arm_group_label>chatbot training</arm_group_label>
      <arm_group_type>Experimental</arm_group_type>
      <description>Ten physician learners will train with the artificial intelligence communication training tool as a behavioral intervention. Before the training, the 10 learners will complete 5 sexual health clinical encounters with adolescent-mother dyads (50 total encounters) where encounters will be audio-recorded and post-encounter surveys competed by each triad member. After training with the tool, the same 10 learners will complete 5 more sexual health clinical encounters with adolescent-mother dyads (50 total encounters) that are audio-recorded and post-encounter surveys completed. The investigators will compare survey results pre and post to evaluate for improvement in communication interactions.</description>
    </arm_group>
    <intervention>
      <intervention_type>Behavioral</intervention_type>
      <intervention_name>chatbot communication training</intervention_name>
      <description>Artificial intelligence chatbot that simulates a mother and a daughter for the pediatrician to practice communication about contraception.</description>
      <arm_group_label>chatbot training</arm_group_label>
    </intervention>
    <eligibility>
      <criteria>
        <textblock>
          Inclusion Criteria (pediatricians/providers):&#xD;
  &#xD;
            -  Pediatricians (residents, fellows, attendings)&#xD;
  &#xD;
            -  Recruited from the Adolescent and Young Adult (AYA) Medicine Clinic at Children's&#xD;
               Medical Center of Dallas.&#xD;
  &#xD;
          Inclusion Criteria (adolescents and young adults):&#xD;
  &#xD;
            -  Adolescents (14-17 years of age)&#xD;
  &#xD;
            -  Young adults (18-21 years of age)&#xD;
  &#xD;
            -  Recruited from the Adolescent and Young Adult (AYA) Medicine Clinic at Children's&#xD;
               Medical Center of Dallas.&#xD;
  &#xD;
          Inclusion Criteria (caregivers):&#xD;
  &#xD;
            -  Caregiver (1) of recruited patients who attend the encounter with the patients&#xD;
  &#xD;
            -  Recruited from the Adolescent and Young Adult (AYA) Medicine Clinic at Children's&#xD;
               Medical Center of Dallas.&#xD;
  &#xD;
          Exclusion Criteria:&#xD;
  &#xD;
            -  Adolescent/young adult is not an active patient&#xD;
  &#xD;
            -  Unable to speak or read in Spanish/English&#xD;
        </textblock>
      </criteria>
      <gender>All</gender>
      <minimum_age>14 Years</minimum_age>
      <maximum_age>N/A</maximum_age>
      <healthy_volunteers>Accepts Healthy Volunteers</healthy_volunteers>
    </eligibility>
    <overall_official>
      <last_name>Jenny K Rodriguez Francis, MD</last_name>
      <role>Principal Investigator</role>
      <affiliation>UTSouthwestern</affiliation>
    </overall_official>
    <overall_contact>
      <last_name>Stephanie M Reyes, MA</last_name>
      <phone>214-456-6792</phone>
      <email>stephanie.reyes@utsouthwestern.edu</email>
    </overall_contact>
    <overall_contact_backup>
      <last_name>Jenny K Francis, MD</last_name>
      <phone>214-456-6792</phone>
      <email>jenny.francis@utsouthwestern.edu</email>
    </overall_contact_backup>
    <location>
      <facility>
        <name>Children's Health Adolescent and Young Adult Medicine (AYA) Clinic</name>
        <address>
          <city>Dallas</city>
          <state>Texas</state>
          <zip>75207</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Jenny K Francis, MD</last_name>
        <phone>214-456-6792</phone>
        <email>jenny.francis@utsouthwestern.edu</email>
      </contact>
      <contact_backup>
        <last_name>May Lau, MD</last_name>
        <phone>214-456-9099</phone>
        <email>may.lau@utsouthwestern.edu</email>
      </contact_backup>
    </location>
    <location>
      <facility>
        <name>UT Southwestern Medical Center</name>
        <address>
          <city>Dallas</city>
          <state>Texas</state>
          <zip>75390-0966</zip>
          <country>United States</country>
        </address>
      </facility>
      <contact>
        <last_name>Stephanie M Reyes, MA</last_name>
        <email>stephanie.reyes@utsouthwestern.edu</email>
      </contact>
      <contact_backup>
        <last_name>Jenny K Rodriguez Francis, MD</last_name>
        <phone>214-456-6792</phone>
        <email>jenny.francis@utsouthwestern.edu</email>
      </contact_backup>
    </location>
    <location_countries>
      <country>United States</country>
    </location_countries>
    <verification_date>August 2023</verification_date>
    <study_first_submitted>May 16, 2023</study_first_submitted>
    <study_first_submitted_qc>August 15, 2023</study_first_submitted_qc>
    <study_first_posted type="Actual">August 16, 2023</study_first_posted>
    <last_update_submitted>August 15, 2023</last_update_submitted>
    <last_update_submitted_qc>August 15, 2023</last_update_submitted_qc>
    <last_update_posted type="Actual">August 16, 2023</last_update_posted>
    <responsible_party>
      <responsible_party_type>Principal Investigator</responsible_party_type>
      <investigator_affiliation>University of Texas Southwestern Medical Center</investigator_affiliation>
      <investigator_full_name>Jenny Francis</investigator_full_name>
      <investigator_title>Associate Professor</investigator_title>
    </responsible_party>
    <keyword>Adolescent</keyword>
    <keyword>Contraception</keyword>
    <keyword>Training</keyword>
    <patient_data>
      <sharing_ipd>Yes</sharing_ipd>
      <ipd_description>In total, this study will collect audio recordings from 150 clinical encounters (transcripts) of adolescents 14-21 years, the caregiver and the pediatrician; 150 electronic health record queries (demographic, medical history, contraceptive prescriptions for the adolescent's encounter), 450 post-encounter surveys, and 10 intervention surveys.&#xD;
  Identifiable data will be de-identified prior to submission. Participant-level data will be preserved and shared. The raw interview data will be stored within a secure computing environment. All direct respondent identifiers (e.g., names and medical records) will be removed and maintained in a secure file for future contact purposes.&#xD;
  The study protocol, data tracking forms, survey, code book, data dictionary and a glossary of domain-specific terms will be submitted in a portable document format (PDF).</ipd_description>
      <ipd_info_type>Study Protocol</ipd_info_type>
      <ipd_info_type>Statistical Analysis Plan (SAP)</ipd_info_type>
      <ipd_info_type>Informed Consent Form (ICF)</ipd_info_type>
      <ipd_info_type>Analytic Code</ipd_info_type>
      <ipd_time_frame>12mo after collection and available per Institutional Review Board standards</ipd_time_frame>
      <ipd_access_criteria>All deidentified study data that are not designated as restricted use will be made available as public use data to the research community via DSDR.</ipd_access_criteria>
    </patient_data>
    <!-- Results have not yet been posted for this study                                          -->
  </clinical_study>
  
  
text: download date: ClinicalTrials.gov processed this data on September 20, 2023
  link text: Link to the current ClinicalTrials.gov record.
  url: https://clinicaltrials.gov/ct2/show/NCT05995574
  org study id: STU-2023-0462
  nct id: NCT05995574
  lead sponsor: 
  has dmc: No
  is fda regulated drug: No
  is fda regulated device: No
  In order to support the desire of most adolescents to delay pregnancy (parenting) until their
        own adulthood, pediatricians must be comfortable and skilled in having reproductive health
        conversations with adolescents and the mothers of adolescents. Artificial intelligence, such
        as a chatbot, could be programmed to simulate the perspective of the mother or the daughter
        as a tool for pediatricians to practice communication before interacting with "real"
        families. Through human-centered design, an iterative problem-solving approach, our overall
        goal is to develop and test a communication training chatbot tool that is accurate,
        developmentally tailored for adolescents and mothers, culturally tailored, and aids
        pediatricians to manage resistance and conflict about contraception to ultimately close
        disparities in teen births. The investigator's primary hypothesis is that a communication
        focused intervention will improve pediatrician interactions with dyads about contraception.
        Through three stages, the study team will develop an artificial intelligence tool as a
        behavioral intervention. The study team will gather basic communication data to generate,
        refine, modify, adapt, and pilot test novel communication tools with real-world encounters
        while keeping humans at the center of attention
  In Stage 1 (formative), the investigators will characterize communication content and styles
        of pediatricians (residents, fellows, attendings) and adolescent-mother dyads on
        contraception discussions with 50 real-life clinical encounters. Through separate
        post-encounter surveys of each pediatrician-adolescent-parent "triad" using validated scales,
        the investigators will describe the overall quality of the interactions (concordant
        contraception plans, satisfaction), communication (confidence, comfort, self-efficacy,
        confidentiality) and decision-making (perceived autonomy, ownership, and power) to determine
        "ideal, moderate and poor" communication interactions. Then, separate group interviews (n =
        15, with adolescents, mothers and physicians) will review recorded vignettes to identify
        optimal culturally tailored communication strategies. In Stage 2 (development), the
        investigators will upload de-identified and anonymized vignettes to an AI communication
        training tool to develop and refine the tool through prompt engineering and performance
        evaluation with adolescent, parent and pediatrician advisory boards. Then, the investigators
        will iteratively test the acceptability and feasibility of the tool on 10 pediatrician
        learners. In Stage 3 (proof-of-concept), the investigators will test how an AI communication
        intervention tool can improve reproductive health encounters by comparing evaluation results
        of 50 pre-training and 50 post-training real encounters. This study will advance pediatrician
        communication training, empower adolescent sexual health, support mothers' priorities, and
        test how AI communication training chatbots can improve triadic interactions.
  allocation: N/A
  intervention model: Single Group Assignment
  intervention model description: single intervention arm with pre- and post- evaluation.
  primary purpose: Health Services Research
  masking: None (Open Label)
  measure: Questionnaire
  time frame: Pre-training, baseline.
  description: Multiple questions will be aggregated to arrive at one reported value to determine "ideal, moderate and poor" communication interactions.
  measure: Questionnaire
  time frame: Post-training, through study completion, an average of 1 year.
  description: Multiple questions will be aggregated to arrive at one reported value to determine "ideal, moderate and poor" communication interactions after training.
  measure: Acceptability Questionnaire
  time frame: Through study completion, an average of 1 year.
  description: Weiner's implementation outcome measure Acceptability of Intervention Measure (AIM). Items are measured on a 5-point Likert scale (Completely Disagree-Completely Agree). Score is calculated mean with the higher score meaning acceptability.
  measure: Feasibility Questionnaire
  time frame: Through study completion, an average of 1 year.
  description: Weiner's implementation outcome measure Feasibility of Intervention Measure (FIM). Items are measured on a 5-point Likert scale (Completely Disagree-Completely Agree). Score is calculated mean with the higher score meaning feasibility.
  arm group label: chatbot training
  arm group type: Experimental
  description: Ten physician learners will train with the artificial intelligence communication training tool as a behavioral intervention. Before the training, the 10 learners will complete 5 sexual health clinical encounters with adolescent-mother dyads (50 total encounters) where encounters will be audio-recorded and post-encounter surveys competed by each triad member. After training with the tool, the same 10 learners will complete 5 more sexual health clinical encounters with adolescent-mother dyads (50 total encounters) that are audio-recorded and post-encounter surveys completed. The investigators will compare survey results pre and post to evaluate for improvement in communication interactions.
  intervention type: Behavioral
  intervention name: chatbot communication training
  description: Artificial intelligence chatbot that simulates a mother and a daughter for the pediatrician to practice communication about contraception.
  arm group label: chatbot training
  criteria: 
  gender: All
  minimum age: 14 Years
  maximum age: N/A
  healthy volunteers: Accepts Healthy Volunteers
  last name: Jenny K Rodriguez Francis, MD
  role: Principal Investigator
  affiliation: UTSouthwestern
  last name: Stephanie M Reyes, MA
  phone: 214-456-6792
  email: stephanie.reyes@utsouthwestern.edu
  last name: Jenny K Francis, MD
  phone: 214-456-6792
  email: jenny.francis@utsouthwestern.edu
  facility: 
  contact: 
  contact backup: 
  facility: 
  contact: 
  contact backup: 
  country: United States
  responsible party type: Principal Investigator
  investigator affiliation: University of Texas Southwestern Medical Center
  investigator full name: Jenny Francis
  investigator title: Associate Professor
  sharing ipd: Yes
  ipd description: In total, this study will collect audio recordings from 150 clinical encounters (transcripts) of adolescents 14-21 years, the caregiver and the pediatrician; 150 electronic health record queries (demographic, medical history, contraceptive prescriptions for the adolescent's encounter), 450 post-encounter surveys, and 10 intervention surveys.
  Identifiable data will be de-identified prior to submission. Participant-level data will be preserved and shared. The raw interview data will be stored within a secure computing environment. All direct respondent identifiers (e.g., names and medical records) will be removed and maintained in a secure file for future contact purposes.
  The study protocol, data tracking forms, survey, code book, data dictionary and a glossary of domain-specific terms will be submitted in a portable document format (PDF).
  ipd info type: Study Protocol
  ipd info type: Statistical Analysis Plan (SAP)
  ipd info type: Informed Consent Form (ICF)
  ipd info type: Analytic Code
  ipd time frame: 12mo after collection and available per Institutional Review Board standards
  ipd access criteria: All deidentified study data that are not designated as restricted use will be made available as public use data to the research community via DSDR.