Co-designing a Sexual Health App With Immigrant Adolescents: Protocol for a Qualitative Community-Based Participatory Action Research Study
Bibliographic record
Abstract
BACKGROUND: Canada is one of the world's most ethnically diverse countries, with over 7 million individuals out of a population of 38 million being born in a foreign country. Immigrant adolescents (aged 10 to 19 years) make up a substantial proportion of newcomers to Canada. Religious and cultural practices can influence adolescents' sexual attitudes and behaviors, as well as the uptake of sexual and reproductive health (SRH) services among this population. Adolescence is a time to establish lifelong healthy behaviors. Research indicates an alarming gap in adolescents' SRH knowledge, yet there is limited research on the SRH needs of immigrant adolescents in Canada. OBJECTIVE: The purpose of this study is to actively engage with immigrant adolescents to develop, implement, and evaluate a mobile health (mHealth) intervention (ie, mobile app). The interactive mobile app will aim to deliver accurate and evidence-based SRH information to adolescents. METHODS: We will use community-based participatory action research to guide our study. This research project will be conducted in 4 stages based on user-centered co-design principles. In Stage 1 (Empathize), we will recruit and convene 3 adolescent advisory groups in Edmonton, Toronto, and Vancouver. Members will be engaged as coresearchers and receive training in qualitative and quantitative methodologies, sexual health, and the social determinants of health. In Stage 2 (Define and Ideate), we will explore SRH information and service needs through focus group discussions with immigrant adolescents. In Stage 3 (Prototype), we will collaborate with mobile developers to build and iteratively design the app with support from the adolescent advisory groups. Finally, in Stage 4 (Test), we will return to focus group settings to share the app prototype, gather feedback on usability, and refine and release the app. RESULTS: Recruitment and data collection will be completed by February 2023, and mobile app development will begin in March 2023. The mHealth app will be our core output and is expected to be released in the spring of 2024. CONCLUSIONS: Our study will advance the limited knowledge base on SRH and the information needs of immigrant adolescents in Canada as well as the science underpinning participatory action research methods with immigrant adolescents. This study will address gaps by exploring SRH priorities, health information needs, and innovative strategies to improve the SRH of immigrant adolescents. Engaging adolescents throughout the study will increase their involvement in SRH care decision-making, expand efficiencies in SRH care utilization, and ultimately improve adolescents' SRH outcomes. The app we develop will be transferable to all adolescent groups, is scalable in international contexts, and simultaneously leverages significant economies of scale. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/45389.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.100 | 0.071 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.059 | 0.011 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".