Addressing weight bias and discrimination: moving beyond raising awareness to creating change
Bibliographic record
Abstract
Weight discrimination is the unjust treatment of individuals because of their weight. There have been very few interventions to address weight discrimination, due in part to the lack of consensus on key messages and strategies. The objective of the third Canadian Weight Bias Summit was to review current evidence and move towards consensus on key weight bias and obesity discrimination reduction messages and strategies. Using a modified brokered dialogue approach, participants, including researchers, health professionals, policy makers and people living with obesity, reviewed the evidence and moved towards consensus on key messages and strategies for future interventions. Participants agreed to these key messages: (1) Weight bias and obesity discrimination should not be tolerated in education, health care and public policy sectors; (2) obesity should be recognized and treated as a chronic disease in health care and policy sectors; and (3) in the education sector, weight and health need to be decoupled. Consensus on future strategies included (1) creating resources to support policy makers, (2) using personal narratives from people living with obesity to engage audiences and communicate anti-discrimination messages and (3) developing a better clinical definition for obesity. Messages and strategies should be implemented and evaluated using consistent theoretical frameworks and methodologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".