Examining Weight Bias among Practicing Canadian Family Physicians
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to examine the attitudes of practicing Canadian family physicians about individuals with obesity, their healthcare treatment, and perceptions of obesity treatment in the public healthcare system. METHOD: A national sample of Canadian practicing family physicians (n = 400) completed the survey. Participants completed measures of explicit weight bias, attitudes towards treating patients with obesity, and perceptions that people with obesity increase demand on the public healthcare system. RESULTS: Responses consistent with weight bias were not observed overall but were demonstrated in a sizeable minority of respondents. Many physicians also reported feeling frustrated with patients with obesity and agreed that people with obesity increase demand on the public healthcare system. Male physicians had more negative attitudes than females. More negative attitudes towards treating patients with obesity were associated with greater perceptions of them as a public health demand. CONCLUSION: Results suggest that negative attitudes towards patients with obesity exist among some family physicians in Canada. It remains to be determined if physicians develop weight bias partly because they blame individuals for their obesity and its increased demand on the Canadian public healthcare system. More research is needed to better understand causes and consequences of weight bias among health professionals and make efforts towards its reduction in healthcare.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".