Bibliographic record
Abstract
Misinformation or myths about obesity can lead to weight bias and obesity stigma. Counteracting myths with facts and evidence has been shown to be effective educational tools to increase an individuals' knowledge about a certain condition and to reduce stigma.The purpose of this study was to identify common obesity myths within the healthcare and public domains and to develop evidence-based counterarguments to diffuse them. An online search of grey literature, media and public health information sources was conducted to identify common obesity myths. A list of 10 obesity myths was developed and reviewed by obesity experts and key opinion leaders. Counterarguments were developed using current research evidence and validated by obesity experts. A survey of obesity experts and health professionals was conducted to determine the usability and potential effectiveness of the myth-fact messages to reduce weight bias. A total of 754 individuals responded to the request to complete the survey. Of those who responded, 464 (61.5%) completed the survey. All 10 obesity myths were identified to be deeply pervasive within Canadian healthcare and public domains. Although the myth-fact messages were endorsed, respondents also indicated that they would likely not be sufficient to reduce weight bias. Diffusing deeply pervasive obesity myths will require multilevel approaches.
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 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 | 0.037 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".