Effect of subsidies on healthful consumption: a protocol for a systematic review update
Bibliographic record
Abstract
INTRODUCTION: The prevalence of diet-related non-communicable diseases (NCDs) are rapidly increasing in most parts of the world. In order to ameliorate the related public health burden, evidence-informed policies to improve diet need to be implemented. Financial subsidies that promote healthful consumption patterns have the potential to reduce NCD risk and may also reduce inequality if targeted at those of low socio-economic position. This protocol is for an updated systematic review of such evidence. METHODS AND ANALYSIS: A systematic search strategy will be used to identify publications on fiscal intervention studies indexed in Embase, CINAHL, Web of Science, EconLit and PubMed in between January 2013 to February 2019. Two reviewers will independently sift identified citations using prespecified inclusion and exclusion criteria to inform full-text review. The outcomes of interest are: consumption patterns (% change in targeted items and in overall dietary patterns), purchasing patterns (% change) or body mass index. Pretested data capture forms will be used for double data extraction. Any inconsistencies in citation sifting or data extraction will be resolved by a third investigator and study authors will be contacted if needed. Systematic searches will be supplemented by reference checking of key articles. Study quality will be assessed and a narrative summary of findings will be produced. Meta-analyses and exploration of heterogeneity will be completed if appropriate. ETHICS AND DISSEMINATION: The review aims to strengthen findings of the primary studies it incorporates. It will synthesise existing published aggregated patient data and only present further aggregate data. Given this, no concerns are held relating to confidentiality and informed consent due to re-use of patient data.If publications or data with ethical concerns are identified, they will be excluded from the review.Results of the systematic review will be published in full and authors will engage directly with research audiences and key stakeholders to share findings. PROSPERO REGISTRATION NUMBER: CRD42019125013.
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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 | 0.126 | 0.155 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.016 | 0.018 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.094 | 0.016 |
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".