Linked models and theories: a tool for school nutrition policies deployment
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore the pertinence of using an integrated conceptual framework based on several theories and models to guide interviews with adults and youth as a prelude to school nutrition policy (SNP) deployment. Design/methodology/approach Appropriate socio-behavioral and communication theories and models within a social marketing approach were used to build the integrated conceptual framework of this study. The target population consists of 115 multidisciplinary key stakeholders in Lebanon. Directed and semi-structured individual interviews and focus groups were conducted by using questionnaires associated with the variables of the framework. Collected data have been submitted to a thematic qualitative analysis. Findings Combining theories and models increases the potential for understanding the broader determinants of SNP deployment. It is important to choose a holistic theoretical perspective: to study key stakeholders’ perceptions of the facilitators and barriers of SNP development and implementation, to emphasize the active participation of communities and to guide the work of policy and decision makers. Practical implications This research offers perspectives on determinants factors envisaged in the deployment of SNP that help key stakeholders in their promotion and communication practices. Social implications For public policy makers, this research suggests a need to address communities perceptions’ of an eventual SNP deployment. Originality/value The comprehensive integrated conceptual framework proposed in this study amalgamates several variables involved in the process of health promotion under various categories to facilitate SNP deployment.
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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 | 0.028 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".