Obesity management and the Health at Every Size <sup>®</sup> approach: a close-up on Canadian adult obesity clinical practice
Bibliographic record
Abstract
The Health at Every Size® (HAES) approach is briefly discussed in the recently updated Canadian Adult Obesity Clinical Practice Guidelines but seems to lack recognition. Here, we discuss issues participating in these circumstances and how they are likely to hinder their integration into future clinical practice and health policies. The reluctance among some health professionals to reconcile size acceptance with lifestyle changes and health improvements is first discussed. Multiple outcomes have also been raised as an issue that alters the interpretability of HAES study efficacy as opposed to weight loss trials. Preselection of a primary outcome is proposed as a way to deal with this challenge. Other issues, including methodological bias in several HAES studies, the complexity related to program implementation, and the influence of debates among proponents of the approach, have been pointed out as contributing factors. This commentary highlights critical points to be addressed in future HAES studies to stimulate more interest in this approach.
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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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.013 | 0.017 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 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".