Strategies for Supporting and Building Student Resilience in Canadian Secondary and Post-Secondary Educational Institutions
Bibliographic record
Abstract
Communication, problem-solving skills, emotional intelligence, and mental health and well-being are key characteristics of a resilient student. These skills are also needed to navigate increasingly complex life and work environments in the 21st century. In addition, resilient students are dedicated to learning, are focused on academic success, and are better equipped to adapt to change and the evolving workplace. An interdisciplinary team from both secondary and post-secondary educational institutions situated at Ontario Tech University, Oshawa, Canada have collaborated to develop and implement strategies and curricula to support and enhance student resilience. The Mental Health Commission of Canada recommends “increase collaboration between (these) institutions - sharing best practices and processes for effective strategy development, and implementation” to better support student reliance and the successful transition from secondary to post-secondary education. We present the overall rationale and approach taken to support capacity building for student resilience in post-secondary institutions. As well as highlighting specific curricula and virtual strategies implemented (e.g., Graphic Novel, Mandalas, Resiliency Handbook) to engage students in building and maintaining resilience.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.028 | 0.005 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".