Innovations in Practice: ‘Go‐to’ Educator Training on the mental health competencies of educators in the secondary school setting: a program evaluation
Bibliographic record
Abstract
BACKGROUND: Educators can play an important role in early identification and triage of youth with mental disorders. This paper reports findings of a program evaluation of the 'Go-To' Educator Training in a secondary school setting. METHODS: Pre- and posttests were administered. RESULTS: Participant mean scores on mental health competencies changed from 12 (40%) (standard deviation [SD] = 4.3) pretraining to 21 (70%) (SD = 3.3) posttraining, t(119) = 25.6, p < .0001, d = 2.3. Participant attitude mean scores improved from 49.9 (SD = 4.6) pretraining, to 51.5 (SD = 4.2), t(115) = 4.3, p < .0001, d = 0.36. CONCLUSIONS: This training is a useful intervention to help educators identify youth with mental disorders and link them to appropriate services.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".