An evaluation of the impact of a restrictive retail food environment intervention in a rural community pharmacy setting
Bibliographic record
Abstract
BACKGROUND: Sugar-sweetened beverage consumption is associated with morbidity and mortality. The retail food environment influences food and beverage purchasing and consumption. This study assesses the impact of a community pharmacy's removal of sweet beverages on overall community sales of carbonated soft drinks (CSD) in a rural setting. We also examined whether the pharmacy intervention affected CSD sales in the town's other food stores. METHODS: Weekly CSD sales data were acquired from the three food retailers in the town of Baddeck, Nova Scotia (January 1, 2013 to May 8, 2015, n = 123 weeks). Autoregressive integrated moving average (ARIMA) analysis was used to analyse the interrupted time series data and estimate the impact of the pharmacy intervention (September 11, 2014) on overall CSD sales at the community level. Data were analysed in 2015. RESULTS: Before the intervention, the pharmacy accounted for approximately 6 % of CSD sales in the community. After the intervention, declines in total weekly average community CSD sales were not statistically significantly. CSD sales at the other food stores did not increase after the pharmacy intervention. CONCLUSIONS: This study was among the first to examine the impact of a restrictive retail food environment intervention, and found a non-significant decline in CSD sales at the community level. It is the first study to examine a retail food environment intervention in a community pharmacy. Pharmacies may have an important role to play in creating healthy retail food environments.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".