Psychometric Evidence for the School Organizational Conditions for Mental Health Programming Measure: Assessing the Organizational Context for Implementing Evidence-Informed Programming in Ontario Schools
Bibliographic record
Abstract
Programming aimed at promoting positive student mental health and reducing or preventing mental-ill health is common within schools in Ontario. A brief, valid, and scalable measure was needed to assess the organizational conditions, or the capacity, readiness, and resources of schools to successfully implement and sustain this programming. In partnership with School Mental Health Ontario, an intermediary organization that facilitates uptake of student mental health programming in schools across the province, the objectives of the current study were to adapt and evaluate the psychometric properties of the School Organizational Conditions for Mental Health Programming Measure for principals. An 18-item measure was completed by 623 principals across the province (from 35 school boards (i.e., districts); 79% elementary, 16% secondary, and 5% both elementary and secondary). A measure of mental health emergency readiness was used to assess convergent validity. Results from item response theory and exploratory and confirmatory factor analysis supported a final 14-item measure assessing four domains: (1) School Mental Health Leadership, (2) Engagement and Collaboration with External Partners, (3) Mental Health Strategy and Action Planning, and (4) Data-Informed Quality Improvement. The final measure demonstrated excellent internal consistency ( αs = 0.83–0.91) and measurement invariance across elementary and secondary schools, and schools in urban and rural areas. Scores were positively associated but not redundant with mental health emergency readiness ( rs = 0.31 −0.42). The School Organizational Conditions for Mental Health Programming Measure is a brief measure that shows promising psychometric evidence for evaluating the organizational conditions of schools for supporting student mental health-related programming.
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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 | 0.015 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".