Assessing the Consumer Food Environment in Restaurants by Neighbourhood Distress Level across Saskatoon, Saskatchewan
Bibliographic record
Abstract
PURPOSE: To assess the consumer food environment in restaurants in Saskatoon, using the Nutrition Environment Measures Survey for Restaurants (NEMS-R), to examine differences by neighbourhood distress level and to reflect on the need for further refinement of the assessment of restaurant consumer food environments. METHODS: Neighbourhoods were classified as low, middle, or high distress level based on the socioeconomic indicators (income, employment, and education) in the Material Deprivation Index. Differences in restaurant consumer food environments, indicated by mean NEMS-R total and sub-scores, were examined by various restaurant categories and by varying neighbourhood distress levels. RESULTS: Chain coffee shops and pita and sandwich restaurants had higher NEMS-R totals and "Healthy Entrées" sub-scores; however, burger and chicken restaurants and pizza restaurants had more barriers to healthful eating. Although restaurants in lower distress level neighbourhoods generally rated healthier (higher NEMS-R scores), only a few measures (such as "Facilitators" and "Barriers") significantly differed by neighbourhood distress level. CONCLUSIONS: The findings highlight the importance of developing interventions to improve restaurant consumer food environments, especially in neighbourhoods with higher distress levels. The results suggest that reliable measures of the consumer food environment could be developed beginning with what can be measured by NEMS-R.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".