An exploratory analysis to identify behavior change techniques of implementation interventions associated with the implementation of healthy canteen policies
Bibliographic record
Abstract
Empirical studies to disentangle the effects of multicomponent implementation interventions are needed to inform the development of future interventions. This study aims to examine which behavior change techniques (BCTs) primarily targeting canteen manager are associated with school's healthy canteen policy implementation. This is a secondary data analysis from three randomized controlled trials assessing the impact of a "high," "medium," and "low" intensity intervention primarily targeting canteen managers on school's implementation of a healthy canteen policy. The policy required primary schools to remove all "red" (less healthy items) or "banned" (sugar sweetened beverages) items from regular sale and ensure that "green" (healthier items) dominated the menu (>50%). The delivery of BCTs were retrospectively coded. We undertook an elastic net regularized logistic regression with all BCTs in a single model. Five k-fold cross-validation elastic net models were conducted. The percentage of times each strategy remained across 1,000 replications was calculated. For no "red" or "banned" items (n = 162), the strongest BCTs were: problem solving, goal setting (behavior), and review behavior goals. These BCTs were identified in 100% of replications as a strong predictor in the cross-validation elastic net models. For the outcome relating to >50% "green" items, the BCTs problem solving, instruction on how to perform behavior and demonstration of behavior were the strongest predictors. Two strategies were identified in 100% of replications as a strong (i.e., problem solving) or weak predictor (i.e., feedback on behavior). This study identified unique BCTs associated with the implementation of a healthy canteen policy.
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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 | 0.051 | 0.165 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".