Weight bias: a call to action
Bibliographic record
Abstract
Weight-related issues (including excess weight, disordered eating and body concerns) are often considered as comprising distinct domains of 'obesity' and 'eating disorders'. In this commentary we argue that the concept of weight bias is an important variable when considering wellbeing across the spectrum of weight-related issues. We make the following six points in support of this argument: i) weight bias is common and has adverse health consequences, ii) shaming individuals for their body weight does not motivate positive behaviour change, iii) internalized weight bias is particularly problematic, iv) public health interventions, if not carefully thought out, can perpetuate weight bias, v) weight bias is a manifestation of social inequity, and vi) action on weight bias requires an upstream, population-level approach. To achieve sustainable reductions in weight bias at a population level, substantive modifications and collaborative efforts in multiple settings must be initiated. We provide several examples of population-level interventions to reduce weight bias.
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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 | 0.111 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.081 |
| Scholarly communication | 0.013 | 0.043 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.088 | 0.106 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".