School Leaders’ Response to Rising Mental Health Concerns: A Collaborative School-Based Social Worker Pilot
Bibliographic record
Abstract
Mental health challenges among students remain a complex and widespread problem facing school leaders. Though schools are a front-line pathway for providing mental health services, many struggling youth are not receiving the professional help they need (Atkins, Hoagwood, Kutash, & Seidman, 2010; Findlay, 2017). Creative and collaborative solutions are needed to bridge the mental health gap. School leader support is essential to the successful implementation of mental health interventions (Forman, Olin, Hoagwood, Crowe, & Saka, 2009). This paper summarizes the novel approach taken by school and community leaders in one rural Ontario community who jointly piloted a school-based social worker (SBSW) role to support secondary schools in two districts. Interviews with five district leaders (four involved in designing the intervention and the SBSW who piloted the role) reveal conditions necessary for the pilot to take place, as well as leader involvement in setting up, supporting, monitoring the new role. Interviews highlighted multiple ways which the pilot improved student mental well-ness: one-on-one and group counselling sessions, charting fresh pathways networks of support available to students, creating a safe space for student and staff drop-ins, among other benefits to schools and stakeholders involved in the pilot. Results also detail three challenges leaders encountered along the way: stakeholder agreement, role clarity, and the temporary design of the role. Findings underscore how school-based social workers show promise in addressing rising mental health challenges.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".