Bridging the Digital Gap: A Content Analysis of Mental Health Activities on University Websites
Bibliographic record
Abstract
Mental health concerns are common among university and college students. Digital mental health resources and support are offered through university websites. However, the content and type of mental health activities of these institutions have not been analyzed. The aim of this study was to conduct a content analysis of mental health commitment and practices listed on Canadian postsecondary institutional websites. A 27-variable codebook was developed to map the content of all Canadian postsecondary institutions (n = 90). Descriptive statistics were applied to provide a broad snapshot of current institutional wellbeing activities. Nearly all institutions offered crisis response options, and multiple mental health supports through various modalities. However, few institutions had a wellbeing framework (34%), engaged in recent campuswide anti-stigma campaigns (33%), tracked campus wellness activities (13%), monitored student mental health outcomes (13%), and solicited feedback through the wellness center webpages (14%). These outcomes were similar across all geographic regions but statistically significantly different between small, medium, and large institutions. Findings suggest institutions need to address these gaps, provide smaller institutions with greater governmental support for building mental health capacity, and work towards developing a centralized hub for mental health that is accessible, navigable, and considers student needs and preferences.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".