“You can’t categorize lived experiences”: Understanding youth engagement in mobile health in an integrated youth services setting
Bibliographic record
Abstract
Objective: Youth mental health is a global challenge often compounded by fragmented services limiting access. In Canada, Integrated Youth Services (IYS) provide centralized access to youth health services, with youth engagement playing a key role during development and implementation. This study aims to identify key factors influencing youth engagement in mobile health (mHealth) development within an IYS setting. Methods: We conducted 23 semi-structured interviews with youth, clinical service providers, and non-clinical staff involved in mHealth services at Foundry. The Consolidated Framework for Implementation Research guided deductive thematic analysis. Results: We identified key facilitators and barriers across five domains: Recognizing core value of engagement, improving external coordination, addressing internal organizational challenges, mitigating power imbalances, and tailoring implementation strategies. Conclusion: The findings highlight the need for inclusive approaches, regulatory frameworks, and strategies to engage underrepresented youth. Future efforts should prioritize iterative learning systems and safe, supportive spaces for meaningful youth engagement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".