Creating healthy food environments in recreation and sport settings using choice architecture: a scoping review
Bibliographic record
Abstract
Recreation and sport settings (RSS) are ideal for health promotion, however, they often promote unhealthy eating. Choice architecture, a strategy to nudge consumers towards healthier options, has not been comprehensively reviewed in RSS and indicators for setting-based multi-level, multi-component healthy eating interventions in RSS are lacking. This scoping review aimed to generate healthy food environment indicators for RSS by reviewing peer-reviewed and grey literature evidence mapped onto an adapted choice architecture framework. One hundred thirty-two documents were included in a systematic search after screening. Data were extracted and coded, first, according to Canada's dietary guideline key messages, and were, second, mapped onto a choice architecture framework with eight nudging strategies (profile, portion, pricing, promotion, picks, priming, place and proximity) plus two multi-level factors (policy and people). We collated data to identify overarching guiding principles. We identified numerous indicators related to foods, water, sugary beverages, food marketing and sponsorship. There were four cross-cutting guiding principles: (i) healthy food and beverages are available, (ii) the pricing and placement of food and beverages favours healthy options, (iii) promotional messages related to food and beverages supports healthy eating and (iv) RSS are committed to supporting healthy eating and healthy food environments. The findings can be used to design nested, multipronged healthy food environment interventions. Future research is needed to test and systematically review the effectiveness of healthy eating interventions to identify the most promising indicators for setting-based health promotion in RSS.
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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 | 0.016 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.015 | 0.018 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".