Use of School-Based Health Centres for Mental Health Support in Cape Breton, Nova Scotia
Bibliographic record
Abstract
OBJECTIVE: To determine whether students with self-reported needs for mental health support used school-based health centres (SBHCs) for this purpose. METHOD: A secondary analysis was conducted on self-reported data collected from 1629 high school students from Cape Breton, Nova Scotia. Descriptive statistics and logistic regression analyses were employed to determine the influence of sex, grade, sexual orientation, socioeconomic status (SES), school performance, social involvement, and health risk-taking behaviours on need for mental health support and use of SBHC for that purpose. RESULTS: One-half of surveyed students reported needs for mental health support. Risk for depression was the most commonly reported indicator of need. Only 13% of students visited a SBHC nurse for mental health support, and 4 times as many females than males used the SBHC for this purpose (20.4%, compared with 5.3%, P < 0.001). There was a significantly increased likelihood of use of SBHC for mental health support, given the presence of a greater number of need factors. Multivariate logistic regression determined that female sex (OR 5.57, 95% CI 3.07 to 10.09), lower SES factor (OR 1.19, 95% CI 1.11 to 1.28), sexual health risk-taking behaviours (OR 1.72, 95% CI 1.28 to 2.31), and suicidal behaviour (OR 1.83, 95% CI 1.48 to 2.27) were significantly associated with the use of SBHCs for mental health support. CONCLUSIONS: Substantial need for mental health support and significant unmet need were observed. In particular, male students underused the services relative to their self-reported need. Implications for SBHCs and directions for future research 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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".