Identifying effective behavior change techniques in interventions for enhancing the implementation of school-based policies and/or practices to prevent chronic disease in students: a secondary analysis of a systematic review
Bibliographic record
Abstract
BACKGROUND: School-based interventions improve healthy eating, physical activity, and reduce tobacco, and/or alcohol use in students. While strategies supporting their implementation have been found effective, a comprehensive understanding of the active ingredients [e.g. behavior change techniques (BCTs)] remains unclear. PURPOSE: To describe and examine which BCTs within implementation strategies are associated with increased implementation of school-based interventions targeting healthy eating, physical activity, tobacco, and/or alcohol use in students aged 5-18. METHODS: A secondary analysis was conducted on 39 randomized controlled trials (RCTs) from a 2024 Cochrane review. Individual BCTs within implementation strategies were coded using the BCT taxonomy v1 and mapped to the Behavior Change Technique Ontology (BCTO). Mode of delivery, setting, and source were coded. Meta-regressions using random-effect models assessed the associations between identified BCTs (at the highest level of aggregation of the BCTO) and effective implementation of policies and/or practice (e.g. number of curriculum lessons taught). RESULTS: Eighty-four independent BCTs were identified and meta-regression analysis revealed that out of 14 highest level of aggregation BCTs, "Associative learning" (e.g. Prompt intended action) had a statistically significant association with increased implementation (standard mean difference 0.90, 95% confidence interval 0.08, 1.72; 30 trials), which were primarily delivered face to face and by teachers or researchers. CONCLUSIONS: Our findings suggest that "Associative learning BCTs" should be prioritized in future school-based interventions to address implementation barriers and increase implementation of policies and/or practices. Opportunities remain to operationalize and evaluate underrepresented BCTs amenable to school settings in future implementation studies.
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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 | 0.055 | 0.148 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.028 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".