Retailer Responses to Public Consultations on the Adoption of Takeaway Management Zones Around Schools: A Longitudinal Qualitative Analysis
Bibliographic record
Abstract
Background: Takeaway food is often high in calories and served in portion sizes that exceed public health recommendations for fat, salt and sugar. This food is widely accessible in the neighbourhood food environment. As of 2019, of all local authorities in England (n=325), 41 had adopted urban planning interventions that can allow them to manage the opening of new takeaway outlets in "takeaway management zones around schools" (known elsewhere as "exclusion zones"). Before adoption, local authorities undertake mandatory public consultation where responses objecting to proposals can be submitted. Evidence on common objections could be insightful for practitioners and policy-makers considering this intervention. Methods: We included 41 local authorities that adopted a takeaway management zone around schools between 2009 and 2019. We identified and analysed objections to proposals submitted by or on behalf of food retailers and local authority responses to these. We used reflexive thematic analysis with a commercial determinants of health lens to generate themes, and investigated if and how objections and responses changed over time. Results: We generated four themes: The role of takeaways in obesity, Takeaway management zone adoption, Use and interpretation of evidence, and managing external opinions. Despite not being implicated by the adoption of takeaway management zones around schools, planning consultants objected to proposals on behalf of transnational food retailers, however, independent takeaways did not respond. Objections attempted to determine the causes of poor diet and obesity, suggest alternative interventions to address them, undermine evidence justifying proposals, and influence perspectives about local authorities and their intervention. Objections consistently raised the same arguments, but over time became less explicit and expressed a willingness to partner with local authorities to develop alternative solutions. Conclusion: Objections to local authority proposals to adopt an urban planning intervention that can stop new takeaways opening near schools featured strategies used by other industries to delay or prevent population health intervention adoption. Practitioners and policy-makers can use our findings when developing proposals for new takeaway management zones around schools. By using knowledge about their local context and addressing arguments against specific aspects of the intervention, they can pre-empt common objections.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".