Population‐level policy recommendations for the prevention of disordered weight control behaviors: A scoping review
Bibliographic record
Abstract
OBJECTIVE: The aim of this scoping review was to identify recommendations and gaps in knowledge surrounding the prevention of disordered weight control behaviors (DWCBs) through policy. METHOD: A search was conducted in several databases to identify English language articles that described an active policy, recommendation, guideline, or educational curriculum that could be implemented by governments or regulatory bodies to prevent DWCBs or related constructs (e.g., weight stigma, body dissatisfaction). Two researchers independently screened articles with oversight from a third researcher. Data were extracted from the final sample (n = 65) and analyzed qualitatively across all articles and within the domains of education, public policy, public health, industry regulation, and media. RESULTS: Only a single empirical evaluation of an implemented policy to reduce DWCBs was identified. Over one-third of articles proposed recommendations relating to industry regulation and media (n = 24, 36.9%), followed by education (n = 21, 32.3%), public policy (n = 19, 29.2%), and public health (n = 10, 15.4%). Recommendations included school-based changes to curricula, staff training, and anti-bullying policies; legislation to ban weight discrimination; policies informed by strategic science; collaboration with researchers from other fields; de-emphasizing weight in health communications; diversifying body sizes and limiting modified images in media; and restricting the sale of weight-loss supplements. DISCUSSION: The findings of this review highlight gaps in empirically evaluated policies to reduce DWCBs but also promising policy recommendations across several domains. Although some policy recommendations were supported by empirical evidence, others were primarily based on experts' knowledge, highlighting the need for greater research on population-level DWCBs prevention through policy. PUBLIC SIGNIFICANCE: Our scoping review of the evidence on policies for the prevention of disordered weight control behaviors identified several recommendations across the domains of education, public policy, public health, and industry regulation and media. Although few empirical investigations of implemented policies have been conducted, expert recommendations for policies to prevent disordered weight control behaviors among populations are plentiful and warrant future consideration by researchers and policymakers alike.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".