Improving Teacher Preparedness and Capacities: Narrowing the Gap to Support Youth Mental Health in a Catholic High School
Bibliographic record
Abstract
A significant proportion of youth enrolled in Ontario high schools struggle with mental health challenges, and educators lack the necessary readiness and capacities to address these issues. To address the problem of practice (PoP), this dissertation-in-practice (DiP) endeavours to create subject-specific professional learning communities (PLCs) with the dual objective of improving teacher preparation to effectively deal with student mental health challenges and foster collective teacher efficacy (CTE) at Central Catholic High School (CCHS). There is a definite correlation between the recent global pandemic, student well-being, mental health, and academic achievement. Considering this, CCHS must increase teacher interventions, engage policies that promote greater equity, and enhance coordination across its teaching staff and student support systems. The DiP is framed by critical social justice and Catholic social teaching (CST). The combined framework is incorporated into Kotter’s (2014) eight-step change model, which is supplemented by collaborative teacher inquiry (CTI) and appreciative inquiry (AI), to facilitate a comprehensive, school-wide improvement plan to enhance collective teacher efficacy (CTE) and support student mental health. The implementation and communication strategies are guided by the Kotter change model and AI to promote learning throughout the change process.\n Keywords: appreciative inquiry, Catholic social teaching, collective teacher efficacy, collaborative teacher inquiry, critical social justice, faith-based leadership, transformative leadership
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".