The Health at Every Size Paradigm and Obesity: Missing Empirical Evidence May Help Push the Reframing Obesity Debate Forward
Bibliographic record
Abstract
A Health at Every Size (HAES) approach has been proposed to address weight bias and stigma in individuals living with obesity, and more recently articulated as a promising public health approach beyond the prevailing focus on weight status as a health outcome. The purpose of this article is to examine our understanding of HAES within the context of public health approaches to obesity, and to present strengths and limitations of the available evidence. Advancing our understanding of HAES from a public health perspective requires us to move beyond an ideological debate and give greater attention to the need for empirical studies across a range of populations. Only then can the value of HAES, as a weight-neutral, public health approach for the prevention of obesity and other chronic diseases, be fully understood.
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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 | 0.084 | 0.130 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.009 | 0.033 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.009 | 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".