Exploration of factors associated with complete mental health among postsecondary students
Bibliographic record
Abstract
Postsecondary students are at risk for mental health problems and there has been an overall reduction in flourishing in recent years. The aim of this study was to explore factors associated with general mental health among students from one postsecondary institution (n = 1082). The Mental Health Continuum-Short Form was used as the outcome, which categorizes individuals as being languishing (i.e., state of incomplete mental health), moderately mentally healthy, or flourishing (i.e., positive mental health). A multivariable ordered logistic regression analysis was applied to identify factors associated with more favourable dimensions of mental health. Most students were moderately mentally healthy (53%), compared to flourishing (32%), and languishing (15%). Factors significantly associated with positive mental health included students who: (i) participated in meditation, (ii) participated in physical exercise, (iii) frequently experienced challenges that helped them grow, (iv) felt one's campus did enough to protect students from COVID-19, and (v) felt they were part of a campus that looked out for others. This provides further evidence for investing in mental health training for instructors, promoting positive coping strategies within the student population, campuswide implementation of a universal approach to health promotion, and building a sense of community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".