What's it gonna take? Lessons learned for youth-friendly mental health services research
Bibliographic record
Abstract
Introduction: Over half of the children and youth with mental illness do not receive appropriate or adequate treatment in both Canada and the United States. The burden of mental illness and substance use is the leading cause of disability due to years lost to disability, leading to the youth mental health crisis. Despite ongoing efforts to improve mental health and substance use services, many youth disengage prematurely, with evidence that this leads to poorer outcomes. In this paper, we explore the question: how to use youth-friendly methods in research for service improvement. Methods: We used innovative and participatory action mixed methods. Youth between the ages of 12 and 25, with lived experience accessing mental health and addiction services, were recruited for focus groups. The focus groups were stratified based on their level of service needs, and data were analyzed using thematic analysis. The themes were interpreted into a fictional narrative summarized in an animated video. This video was embedded in a survey that was sent to the participants. The purpose was to validate the analysis and explore the factors that led them to participate. A descriptive analysis of the quantitative data and an inductive content analysis of the qualitative data were completed for the survey. Results: A total of 44 youth completed the screening to stratify the level of need. Fourteen youth participated in three pilot focus groups, and another 24 participated in four focus groups stratified by need. The mean age was 22.3 years, and 78% and 22% identified as male and female, respectively. Youth-friendly research was the main theme, with two main sub-themes: youth want to participate in research, and there were strategies for research approaches involving youth service users. Fundamentally, choice throughout the process was important. Conclusion: Youth service users want to be engaged meaningfully. Youth are not afraid to speak their truth and want opportunities to provide their unique perspectives. Service improvements from youth service-user feedback may lead to improved outcomes with full treatment because youth remain engaged with services. Service improvement may need youth-friendly research.
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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 | 0.125 | 0.101 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.021 | 0.024 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.009 | 0.018 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".