Providing Measurement, Evaluation, Accountability, and Leadership Support (MEALS) for Non-communicable Diseases Prevention in Ghana: Project Implementation Protocol
Bibliographic record
Abstract
Background: This study describes the rationale, adaptation, and final protocol of a project developed to address the increase in obesity and nutrition-related non-communicable diseases (NR-NCDs) in Ghana. Code-named the Measurement, Evaluation, Accountability, and Leadership Support for NCDs (MEALS4NCDs) project, it aims to measure and support public sector actions that create healthy food marketing, retail, and provisioning environments for Ghanaian children using adapted methods from the International Network for Food and Obesity/NCDs Research Monitoring and Action Support (INFORMAS). Methods: The protocol for this observational study draws substantially from the INFORMAS' Food Promotion and Food Provision Modules. However, to appraise the readiness of local communities to implement interventions with strong potential to improve food environments of Ghanaian children, the MEALS4NCDs protocol has innovatively integrated a local community participatory approach based on the community readiness model (CRM) into the INFORMAS approaches. The setting is Ghana, and the participants include health and nutrition policy-makers, nutrition and food service providers, consumers, school authorities, and pupils of Ghanaian basic schools. Results: The study establishes a standardized approach to providing implementation science evidence for the prevention of non-communicable diseases (NCDs) in Ghana. It demonstrates feasibility and the innovative application of the INFORMAS expanded food promotion and food provision modules, together with the integration of the CRM in a lower-middle income setting. Conclusion: The research will facilitate the understanding of the processes through which the INFORMAS approach is contextualized to a lower-middle income African context. The protocol could be adapted for similar country settings to monitor relevant aspects of food environments of children.
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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 | 0.117 | 0.075 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.015 |
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".