A collaborative, school-based wraparound support intervention for fostering children and youth's mental health
Bibliographic record
Abstract
Early mental health interventions are needed in response to a growing mental health crisis among children and youth. Schools are promising sites for early intervention because they have existing infrastructure for engaging with students. Specifically, collaborative initiatives involving community partnerships allow schools to leverage shared resources to deliver mental health support. However, more research is needed to guide the development of early interventions so that they effectively address students' mental health needs. The present study explored the role of collaborative, school mental health services in fostering children and youth's mental health, through All in for Youth, a wraparound model of support in Edmonton, Canada. Three research questions were addressed: What mental health concerns do children and youth experience? What are the factors that impact the use of collaborative school mental health services? Do collaborative school mental health services lead to perceived mental health impacts among children and youth? A multiple methods secondary analysis was conducted on school cohort data across seven elementary and junior high schools (n= 2,073 students), and interview and focus group data (n= 51 students, grades 2–9;n= 18 parents/caregivers). The quantitative findings indicated that 42.7% of students accessed any type of mental health service across the schools, with close to equivalent service use by gender (50.2% male, 49.5% female, 0.3% genderqueer) and grade (kindergarten-grade 9;M= 10%, SD = 1.9%, range = 6.3%−13%). Participants accessed mental health services in primarily individual or combined individual and group settings (72.9%) and as an informal client (75.1%). The interview and focus group findings revealed high mental health needs among students, which were exacerbated by the COVID-19 pandemic. In response to these needs, a supportive school culture, adequate school communication, and a stable and well-resourced mental health workforce promoted access to collaborative school mental health services. Finally, mental health services supported children and youth through the experience of having a supportive relationship with a safe and caring adult, an improved capacity to cope with school and life, and improved family functioning. The findings underscore the importance of developing school mental health services that take an ecological, wraparound approach to addressing students' multi-faceted mental health needs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".