Industry views of the UK Soft Drinks Industry Levy: a thematic analysis of elite interviews with food and drink industry professionals, 2018–2020
Bibliographic record
Abstract
Objectives The UK Soft Drinks Industry Levy (SDIL), implemented in 2018, has been successful in reducing the sugar content and purchasing of soft drinks, with limited financial impact on industry. Understanding the views of food and drink industry professionals involved in reacting to the SDIL is important for policymaking. However, their perceptions of the challenges of implementation and strategic responses are unknown. The aim of this study, therefore, was to explore how senior food and drink industry professionals viewed the SDIL. Design We undertook a qualitative descriptive study using elite interviews. Data were analysed using Braun and Clarke’s thematic analysis, taking an inductive exploratory and descriptive approach not informed by prior theory or frameworks. Setting and participants Interviews were conducted via telephone with 14 senior professionals working in the food and drink industry. Results Five main themes were identified: (1) a level playing field…for some ; industry accepted the SDIL as an attempt to create a level playing field but due to the exclusion of milk-based drinks, this was viewed as inadequate, (2) complex to implement, but no lasting negative effects ; the SDIL was complex, expensive and time consuming to implement, with industry responses dependent on leadership buy-in, (3) why us?—the SDIL unfairly targets the drinks industry ; soft drinks are an unfair target when other categories also contain high sugar, (4) the consumer is king ; consumers were a key focus of the industry response to this policy and (5) the future of the SDIL ; there appeared to be a wider ripple effect, which primed industry to prepare for future regulation in support of health and environmental sustainability. Conclusions Insights from senior food and drink industry professionals illustrate how sugar-sweetened beverage taxes might be successfully implemented and improve understanding of industry responses to taxes and other food and drink policies. Trial registration number ISRCTN18042742 .
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| 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".