Intervention Activities and Implementation Strategies for School-Based Health Promotion: Identifying Core Functions and Forms to Facilitate Scale-up of an Effective Intervention
Bibliographic record
Abstract
Background: School-based health promotion is a key public health strategy to reduce disease burden and health inequalities. School-based interventions with local evidence of effectiveness need to be scaled up to maximize their benefits. A Project Promoting healthy Living for Everyone in Schools (APPLE Schools) is a health promoting school (HPS) intervention that targets schools in disadvantaged settings and has been shown to be effective in promoting children's healthy lifestyle behaviors and reducing health inequalities. To support its scale-up, we aimed to identify core functions (basic purposes driving intervention's effectiveness) and forms (specific content and delivery strategies implemented to achieve core functions). Method: We extracted 5,301 action items from 191 annual action plans written between 2011 and 2021 in 70 APPLE Schools. We followed an implementation science approach and used supervised machine learning algorithms to classify 2,683 unique action items into intervention activities and implementation strategies. Core functions were drawn from theoretical frameworks; forms were identified through thematic analysis. Results: We identified 55 forms and mapped them to 17 core functions of intervention activities and implementation strategies. The most common core functions of intervention activities were enablement (96%), modeling (66%), and education (54%); the most common core functions of implementation strategies were relational and organizational support context (86%), partnerships and networking (84%), student participation (78%), and professional development and learning (73%). The remaining core functions were identified in <50% of the schools. Forms included a broad range of activities, with a greater variety of those that addressed the most common core functions. Conclusions: We created matrices of core functions and forms of intervention activities and implementation strategies to inform the successful scale-up of APPLE Schools, an effective and cost-effective HPS intervention. These matrices can be used as a guide to improving existing HPS interventions and scaling them up to new settings.
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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 | 0.058 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".