Measuring nutrition environments: a comparison between instrument evaluation in developed and developing countries.
Bibliographic record
Abstract
Background : Nutrition environments can either be supportive or detrimental to population health. Healthy nutrition environments are equally important in both developed and developing regions, however rapidly urbanizing areas of the developing world are facing the double burden of nutrition as they deal with both hunger and obesity simultaneously. A number of instruments aimed at measuring nutrition environments have been created and evaluated, though most of this evaluation has taken place in developed settings. Methods : A literature review was undertaken in order to identify journal articles that evaluate instruments that measure nutrition environments and assess their applicability to different settings. Two separate searches returned 48 relevant articles with 20 included in this review. Eighteen articles were from developed settings. Most included articles reported testing of reliability and validity of the measurement instruments. Discussion : There is a lack of literature describing the measurement of nutrition environments in developing cities, however the NEMS instrument developed by Glanz et al. has been implemented in Brazil. Instruments measuring nutrition environments have several shortcomings, and caution should be used when extrapolating results about the environment to the behaviour of individuals. Recommendations : Based on this review it appears that the NEMS instruments is the most likely to be successfully modified to unique settings, and could be implemented in developing cities to measure their nutrition environment. The indicator foods used in the modified instrument should be well researched and applicable to each unique setting.
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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 | 0.127 | 0.187 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".