Institutional Factors Affecting Postsecondary Student Mental Wellbeing: A Scoping Review of the Canadian Literature
Bibliographic record
Abstract
There have been increased calls to address the growing mental health concerns of postsecondary students in Canada. Health promotion focuses on prevention and is needed as part of a comprehensive approach to student mental health support, with an emphasis on not just the individual but also the sociocultural environment of postsecondary institutions. The aim was to conduct a scoping review of the literature pertaining to the associations between postsecondary institutional factors and student wellbeing. The review included a comprehensive search strategy, relevance screening and confirmation, and data charting. Overall, 33 relevant studies were identified. Major findings provide evidence that institutional attitudes, institutional (in)action, perceived campus safety, and campus climate are associated with mental wellbeing, suggesting that campus-wide interventions can benefit from continued monitoring and targeting these measures among student populations. Due to the large variability in reporting and measurement of outcomes, the development of standardized measures for measuring institutional-level factors are needed. Furthermore, institutional participation and scaling up established population-level assessments in Canada that can help systematically collect, evaluate, and compare findings across institutions and detect changes in relevant mental health outcomes through time.
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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 | 0.011 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.020 | 0.027 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".