Gaps in the Evidence on Population Interventions to Reduce Consumption of Sugars: A Review of Reviews
Bibliographic record
Abstract
There is currently considerable attention directed to identifying promising interventions to reduce consumption of sugars among populations around the world. A review of systematic reviews was conducted to identify gaps in the evidence on such interventions. Medline, EMBASE CINAHL, and the Cochrane Database of Systematic Reviews were searched to identify systematic reviews published in English from January 2005 to May 2017 and considering research on interventions to reduce sugar intake. Twelve systematic reviews that considered price changes, interventions to alter the food available within specific environments, and health promotion and education programs were examined. Each of the identified reviews focused on sugar-sweetened beverages (SSBs). The existing literature provides some promising indications in terms of the potential of interventions to reduce SSB consumption among populations. However, a common thread is the limited scope of available evidence, combined with the heterogeneity of methods and measures used in existing studies, which limits conclusions that can be reached regarding the effectiveness of interventions. Reviewed studies typically had limited follow-up periods, making it difficult to assess the sustainability of effects. Further, there is a lack of studies that address the complex context within which interventions are implemented and evaluated, and little is known about the cost-effectiveness of interventions. Identified gaps speak to the need for a more holistic approach to sources of sugars beyond SSBs, consensus on measures and methods, attention to the implementation of interventions in relation to context, and careful monitoring to identify intended and unintended consequences.
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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 | 0.011 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".