Development of physical activity and food built environment quality indicators for chronic diseases in Argentina
Bibliographic record
Abstract
Although obesity and non-communicable disease (NCD) prevention efforts to-date have focused mainly on individual level factors, the social and physical environments in which people live are now widely recognized as important social determinants of health. Obesogenic environments promote higher dietary energy intakes and sedentary behaviors, thus contributing to the obesity/NCD burden. To develop quality indicators (QIs) for measuring food and physical activity (PA)-built environments in municipalities. A literature review was conducted. Based on the best practices identified from this review, a draft set of candidate QI was retrieved. The initial 67 QIs were then evaluated by a modified Delphi panel of multidisciplinary health professionals (n = 40) to determine their relevance, validity, and feasibility in 3 rounds of voting and threaded discussion using a modified RAND/University of California, Los Angeles Appropriateness Methodology. Response rate for the panel was 89.4%. All final 42 QIs were rated as highly relevant, valid, and feasible (median rating ≥ 7 on a 1-9 scale), with no significant disagreement. The final QI set addresses for the PA domain: (i) promotion of PA; and (ii) improvements in the environment to strengthen the practice of PA; and for Food environment domain: (i) promotion of healthy eating; (ii) access to healthy foods; and (iii) promotion of responsible advertising. We generated a set of indicators to evaluate the PA and food built environment, which can be adapted for use in Latin American and other low- and middle-income countries.
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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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".