Prioritizing Mental Well-being in Emerging Educational Models: Strategies for Integrating Social and Emotional Learning (SEL) to Support Student Mental Health
Bibliographic record
Abstract
This paper conducts a comprehensive review of the effectiveness of integrating Social and Emotional Learning (SEL) practices into emerging educational models to support student mental well-being, with a particular focus on Canadian contexts. Employing a systematic literature review methodology, the study utilized Google Scholar as the primary database, filtering results based on inclusion and exclusion criteria, emphasizing relevance, quality, and recency. The main findings underscore a research gap between acknowledgment of the importance of SEL, and its implementation in Canadian schools. Strategies for integrating SEL into educational settings are reviewed, highlighting ongoing initiatives of British Columbia aimed at advancing SEL within teacher training programs and educational policies. The study also highlights escalating concerns regarding the mental health of Canadian children and youth, impaired by the COVID-19 pandemic. Implications for educational policy and practice, including promoting systematic assessment of students’ social-emotional skills and ensuring the appropriate developmental delivery of SEL through content, are emphasized as key considerations for educational policymakers and practitioners. The paper concludes that a holistic approach is advocated for fostering Canadian children and youth's social-emotional skills, crucial for their well-being and success in educational settings.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".