Advancing socioecological mental health promotion intervention: A mixed methods exploration of Phase 1 Agenda Gap findings
Bibliographic record
Abstract
Introduction: Protecting and promoting the mental health of youth under 30 years of age is a priority, globally. Yet investment in mental health promotion, which seeks to strengthen the determinants of positive mental health and wellbeing, remains limited relative to prevention, treatment, and recovery. The aim of this paper is to contribute empirical evidence to guide innovation in youth mental health promotion, detailing the early outcomes of Agenda Gap, an intervention centering youth-led policy advocacy to influence positive mental health for individuals, families, communities and society. Methods: Leveraging a convergent mixed methods design, this study draws on data from n = 18 youth (ages 15 to 17) in British Columbia, Canada, who contributed to pre- and post-intervention surveys and post-intervention qualitative interviews following their participation in Agenda Gap from 2020-2021. These data are supplemented by qualitative interviews with n = 4 policy and other adult allies. Quantitative and qualitative data were analyzed in parallel, using descriptive statistics and reflexive thematic analysis, and then merged for interpretation. Results: Quantitative findings suggest Agenda Gap contributes to improvements in mental health promotion literacy as well as several core positive mental health constructs, such as peer and adult attachment and critical consciousness. However, these findings also point to the need for further scale development, as many of the available measures lack sensitivity to change and are unable to distinguish between higher and lower levels of the underlying construct. Qualitative findings provided nuanced insights into the shifts that resulted from Agenda Gap at the individual, family, and community level, including reconceptualization of mental health, expanded social awareness and agency, and increased capacity for influencing systems change to promote positive mental health and wellbeing. Discussion: Together, these findings illustrate the promise and utility of mental health promotion for generating positive mental health impacts across socioecological domains. Using Agenda Gap as an exemplar, this study underscores that mental health promotion programming can contribute to gains in positive mental health for individual intervention participants whilst also enhancing collective capacity to advance mental health and equity, particularly through policy advocacy and responsive action on the social and structural determinants of mental health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".