A scoping review of studies applying the Nuffield’s ‘intervention ladder’ framework to assess the acceptability of diet and physical activity interventions
Bibliographic record
Abstract
BACKGROUND: The Nuffield's Intervention Ladder (NIL) framework casts public acceptability of health interventions based on their level of intrusiveness- how much they restrict personal autonomy and freedom of choice. This scoping review explores the application of the NIL framework in assessing public acceptability of diet and physical activity interventions, identifying key trends, gaps, and alignment with the framework's conceptual underpinnings. METHODS: We searched six databases (PubMed, Scopus, Medline, Embase, Science Direct, and Web of Science) and included 15 eligible studies. Data were charted and synthesized thematically and narratively. RESULTS: The NIL framework was applied across different study designs, primarily post hoc, to categorize interventions based on their intrusiveness. Consistent with the framework, less intrusive interventions (information provision, enabling choice) were widely accepted. Moderately intrusive interventions (changing defaults, incentives, and disincentives) received mixed public acceptance, whereas highly intrusive interventions (restrict and eliminate choice) generally garnered lower public acceptability. Highly intrusive interventions were publicly acceptable when they are directed at children, or at industries. Across all intervention types, demographic and behavioural factors significantly influenced public acceptance. CONCLUSION: The NIL framework offers useful insights into how intrusiveness affects public acceptability of interventions. However, the review highlights that various factors influence acceptability in ways that extend the framework's initial propositions.
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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 | 0.074 | 0.249 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.031 | 0.032 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".