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Record W4415891941 · doi:10.22230/ijepl.2025v21n4a1489

Building Mental Health Capacity in Schools: Early Lessons from a Canadian Initiative

2025· article· en· W4415891941 on OpenAlexaffvenueabout
Sharon Friesen, Stephen MacGregor, Dennis Sumara, Jennifer Turner, Brenna Mesner

Bibliographic record

VenueInternational Journal of Education Policy and Leadership · 2025
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsQueen's UniversityUniversity of Calgary
Fundersnot available
KeywordsMental healthMental health serviceService (business)Strategic planningBest practiceService providerWorkforce development

Abstract

fetched live from OpenAlex

This article examines the early implementation of a provincial initiative aimed at improving mental health supports in Canadian elementary and secondary schools. A content analysis of 60 proposals submitted by school jurisdictions uncovered the organizational and cultural strengths and challenges that influence the development of mental health initiatives. The analysis revealed three strengths: readiness for change, tailored mental health solutions, and strategic leadership. Five challenges also emerged: fragmented service delivery, an overreliance on external service providers, a lack of qualified staff, difficulties in forming authentic partnerships with families and communities, and impediments to establishing an effective organizational infrastructure. The findings highlight the complexity of embedding mental health initiatives in schools and the importance of sustained leadership, professional learning, and community collaboration for long-term integration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.010
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.821
Threshold uncertainty score0.953

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0300.010
Scholarly communication0.0080.003
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.323
GPT teacher head0.523
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes3
Has abstractyes

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