Supporting individuals’ healthy eating requires genuine engagement with communities
Bibliographic record
Abstract
Abstract C3 Collaborating for Health (C3) aims to counter the non‐communicable disease (NCD) epidemic by focusing on the three big risk factors: tobacco use, poor diet and lack of physical activity. Community Health Engagement Survey Solutions (CHESS™) is an innovative strategy to shift decision‐making around prevention and health interventions to local communities, while also reducing inequalities in the broader determinants of health. Emerging from research in India, China, Mexico and the US, C3 has implemented CHESS™ in the UK in eight London boroughs, Halifax and Girvan. A recently completed project in England and Scotland engaged 5000 people (approximately one‐third of the local populations). CHESS™ facilitates communities to act as ‘citizen scientists’ in data‐driven investigations about health and the built environment. Through a mobile tool, communities collect and interpret quantitative and qualitative data on local assets and barriers conducive to good health (or not). These results inform evidence‐based action plans, guided by public health expertise, for interventions that make it easier for all to be healthy. The community enacts the changes they can make themselves and presents recommendations to decision‐makers in a compelling argument for change. Thanks to CHESS™ evidence, communities have received over £2 million to implement health interventions in their neighbourhoods. The Healthy Communities project, completed in September 2017, led to physical activity and healthy eating initiatives, including cooking lessons, gardening, breakfast and tea clubs, and healthy lunches for schoolchildren. Learnings from the project have informed recommendations for those wanting to improve community health, particularly in relation to diet.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.026 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".