Mental Health Programs at Canadian Post-Secondary Educational Institutions: A Current State Assessment of Promotion, Outreach and Treatment Services (appears in: Mental Health Services for Students at Postsecondary Institutions: A National Survey). Copyright: Natalia Jaworska, Elisea De Somma, Bernice Fonseka, Emma Heck, Glenda M. MacQueen.
Bibliographic record
Abstract
Full text in Supplementary file on the website. OBJECTIVE: Although the high prevalence of mental health issues among postsecondary students is well documented, comparatively little is known about the adequacy, accessibility, and adherence to best practices of mental health services (MHSs)/initiatives on postsecondary campuses. We evaluated existing mental health promotion, identification, and intervention initiatives at postsecondary institutions across Canada, expanding on our previous work in one Canadian province. METHODS: A 54-question online survey was sent to potential respondents (mainly front-line workers dealing directly with students [e.g., psychologists/counsellors, medical professionals]) at Canada's publicly funded postsecondary institutions. Data were analyzed overall and according to institutional size (small [<2000 students], medium [2000-10 000 students], large [>10 000 students]). RESULTS: In total, 168 out of 180 institutions were represented, and the response rate was high (96%; 274 respondents). Most institutions have some form of mental health promotion and outreach programs, although most respondents felt that these were not a good use of resources. Various social supports exist at most institutions, with large ones offering the greatest variety. Most institutions do not require incoming students to disclose mental health issues. While counselling services are typically available, staff do not reliably have a diverse complement (e.g., gender or race diversity). Counselling sessions are generally limited, and follow-up procedures are uncommon. Complete diagnostic assessments and the use of standardized diagnostic systems are rare. CONCLUSIONS: While integral MHSs are offered at most Canadian postsecondary institutions, the range and depth of available services are variable. These data can guide policy makers and stakeholders in developing comprehensive campus mental health strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".