Predictors of Healthy Youth Relationships Program Implementation in a Sample of Canadian Middle School Teachers
Bibliographic record
Abstract
Implementation of evidence-based, Tier 1 social-emotional learning (SEL) programming that supports healthy relationships skills is recognized as a key mental health promotion and violence prevention strategy for youth. However, work specifically exploring how to support the high-quality implementation of such programming with Canadian teachers and schools is just beginning to emerge. Drawing on implementation frameworks that emphasize the importance of setting, provider, and implementation process characteristics for understanding program implementation outcomes, this prospective, longitudinal study explores implementation of the Fourth R, a SEL-based healthy relationships program, in a Western Canadian province using a sample of middle school teachers. The aim of this mixed-methods study was to illuminate relationships among teachers’ attitudes toward evidence-based programming, perceptions of organizational climate, and training experience with program implementation outcomes (dosage, quality, fidelity). Findings of this exploratory study identified that these characteristics influenced implementation dosage, quality, and fidelity in differential ways. Qualitative data drawn from teacher interviews supported quantitative findings and highlighted the importance of organizational support for high-quality implementation. We discuss areas for further study, given that there are significant gaps in knowledge about teachers’ attitudes toward evidence-based programming, school climate, and other systemic factors in the Canadian context, as well as relevance of study findings to the field of school psychology.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".