Policy coordination to support Manitoban students with mental health and substance misuse
Bibliographic record
Abstract
This dissertation examines the potential for policy coordination in supporting youth with Mental Health and Substance Use Disorders (MHSUD). In particular, it identifies existing barriers and proposes pathways to multi-level cross-sectorial policy coordination supporting youth with MHSUD in Manitoba public schools. Despite aspirations towards restorative MHSUD responses, there is ample research documenting a lack of treatment and support options for youth with MHSUDs (Scheim et al., 2013; Virgo Planning, 2018). Policy fragmentation in Canada’s federalist governance model has resulted in disjointed actions in response to this important issue. Policies that work cooperatively between education, healthcare, social services agencies, Indigenous governments, federal funders and NGOs are vital in order to support Manitoban youth in schools. The purpose of this dissertation is to investigate how policy coordination might be achieved to support legislation, create policy coherence, and secure service provision to better support youth with MHSUD in schools. In particular, this dissertation responds to the research question: How can policy coordination be utilized to improve cross-sector and multi-level support for youth with MHSUDs in Manitoban schools? This dissertation draws on Multi-Level Governance as a conceptual approach, supported by insights from Advocacy Coalition Framework, and uses British Columbia as a comparator to the Manitoban context. Methodologically, this qualitative research project engages in critical discourse analysis, examining current policy documents in Manitoba and British Columbia associated with youth MHSUD in order to map out stakeholders and illuminate both barriers and pathways to coordination, in the aim of making policy recommendations to better support Manitoban youth. Findings suggest that there is a paucity of coordination between sectored actors in Manitoba around the common issue of MHSUD and that this lack of coordination exists in a system with a scarcity of coordinating actors to spur on policy alignment and policy coalitions. Resulting out of a lack of coordination and coordinating actors, schools take on cross-sector work at the grass-roots level, which leads to inequity and barriers to care. BC offers models of higher level coordination and coordinating actors which are compared alongside the Manitoban landscape.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".