Implementing the Mental health and well-being for post-secondary students National Standard of Canada in a Sample of Ontario Colleges
Bibliographic record
Abstract
In July 2020, the Mental Health Commission of Canada and the Canadian Standards Association released the Mental health and well-being for post-secondary students National Standard of Canada (the Standard) (Canadian Standards Association, 2020). This comprehensive, evidence-based standard was intended as a voluntary guiding document for post-secondary institutions to examine their mental health and well-being policies and practices, benchmark their opportunities, challenges and gaps, and work towards a greater understanding of the shared responsibility of institutional mental health and well-being in post-secondary education. As student mental health has been an area of increasing concern in post-secondary education concern (Holmes et al., 2011; Diplacito-Derango 2016, 2021; Robinson et al., 2016; Linden & Stuart, 2020; Wiens, et. al, 2020; Grubic et al., 2020, Lipson et al., 2021), and the COVID-19 global pandemic led to increasing complexity of student mental health and well-being more broadly, the Standard provided an opportunity for institutions to take a deep dive into the mental health and well-being pulse in their institutions. In my research I examined four Ontario colleges, and their journey through implementation and compliance with the Standard. Using multiple case study research design and framing my work using the Socio-ecological framework and Kotter’s 8-Step Model of Change, I examined how the four colleges were using the Standard to design, implement and evaluate mental health and well-being strategies, what processes they were using and the challenges the colleges were facing as they move toward the concept of student well-being as a shared responsibility. I analyzed institutional documents, policies and mental health frameworks to gain a better understanding of how these elements fit into the overall scope of the Standard. And I interviewed 13 individuals who had direct knowledge of and responsibility for implementing the Standard in their respective colleges, to examine how each institution was engaging and leveraging internal and external stakeholders to inform the design, implementation and evaluation of mental health and well-being student services. In my concluding chapter, I identify the implications of my findings for policy/practice, further research and theory and conclude with a discussion of the scholarly contribution of my study findings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.005 |
| 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".