Mandated Leave Policies in the Context of Student Mental Health Challenges at Canadian Universities: A Framework Analysis
Bibliographic record
Abstract
Although there is increased attention to the mental health needs of university students, far less attention has been given to mental health-related university policies. Many Canadian public universities have mandated leave policies that specify the conditions under which a student may be required to take a leave of absence from university. The purpose of the current study was to conduct an in-depth analysis of current mandated leave policies in public Canadian English-speaking universities. Applied framework analysis methodology was used to examine the approaches to balancing the needs of students experiencing mental health challenges and providing a safe environment on campus. Three primary themes regarding mandated leave policies were identified, including (a) approaches for addressing mental health concerns, (b) balancing the needs of the student with the needs of the institution, and (c) guidelines, standards, and quality assurance. Implications for mandated leave policies and approaches to students experiencing mental health challenges are discussed.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".