Implementation of Healthy Eating Interventions in Center-Based Childcare: The Selection, Application, and Reporting of Theories, Models, and Frameworks
Bibliographic record
Abstract
PURPOSE: To explore the selection, use, and reporting of theories, models, and frameworks (TMFs) in implementation studies that promoted healthy eating in center-based childcare. DATA SOURCE: We searched 11 databases for articles published between January 1990 and October 2018. We also conducted a hand search of studies and consulted subject matter experts. STUDY INCLUSION AND EXCLUSION CRITERIA: We included studies in center-based settings for preschoolers that addressed the development, delivery, or evaluation of interventions or implementation strategies related to healthy eating and related subjects and that explicitly used TMF. Exclusion criteria include not peer reviewed or abstracts and not in English, French, German, and Korean. DATA EXTRACTION: The first author extracted the data using extraction forms. A second reviewer verified data extraction. DATA SYNTHESIS: Direct content analysis and narrative synthesis. RESULTS: We identified 8222 references. We retained 38 studies. Study designs included quasi-experimental, randomized controlled trials, surveys, case studies, and others. The criteria used most often for selecting TMFs were description of a change process (n = 12; 23%) or process guidance (n = 8; 15%). Theories, models, and frameworks used targeted different socioecological levels and purposes. The application of TMF constructs (e.g., factors, steps, outcomes) was reported 69% (n = 34) of times. CONCLUSION: Reliance on TMFs focused on individual-level, poor TMF selection, and application and reporting for the development of implementation strategies could limit TMF utility.
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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 | 0.296 | 0.655 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.034 | 0.039 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".