School-based mental health literacy training shifts the quantity and quality of referrals to tertiary child and adolescent mental health services: A Western Canada regional study
Bibliographic record
Abstract
BACKGROUND: We aimed to improve mental health referral quality of young people by helping educators build capacity for early identification of youth at risk of mental illness and facilitate referrals between the education and health systems. METHODS: We applied the Go-To Educator mental health literacy training for early identification, triage and support in 208 schools in Calgary, Alberta between 2013 and 2016. Students presenting to mental health services during this time were compared on a number of clinical, system, and demographic variables, based on the training status of the school (untrained schools; before and after training schools), using retrospective cohort design. Based on clinical and system data, bivariate and multivariable logistic regression analysis were employed to compare the three school status domains. RESULTS: After training, referrals differed significantly from control and pre-training schools. Students presenting to services from these schools were younger, from single parent families; were referred more because of adjustment and learning/attention problems; had complex social/family issues; thought disturbances, and harmful behavior/thoughts towards others. While they waited longer to be admitted they stayed longer in services; had more provisional comorbid diagnoses and demonstrated positive treatment outcomes. CONCLUSIONS: The Go-To Educator training may be an effective intervention helping educators identify students at risk of mental disorders and in substantial need of mental health services, demonstrating improved linkages between education and health sectors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".