Weight Bias: Trends Among the Canadian Public and Relationships with Physical Activity and Sedentary Behaviour
Bibliographic record
Abstract
Introduction: Weight bias is a social justice issue in Canada. It is perpetuated by negative attitudes about individuals with obesity and about the causes of obesity. Research on the association between explicit and internalized weight bias and physical activity and sedentary behaviour is limited, especially among population-based samples. Data on weight bias internalization (WBI) and beliefs about the causes of obesity among Canadians is also lacking. \nObjectives: The primary objectives of this study were to describe the level of WBI among Canadians and describe how Canadians attribute obesity to different causes; and to examine the relationships between weight bias and physical activity and sedentary behaviour. \nMethods: A sample of Canadian adults (N = 942; 51% female; mean body mass index [BMI]= 27.3 ± 6.7 kg/m2) completed an online survey. Questionnaires included the Anti-Fat Attitudes Questionnaire, Modified Weight Bias Internalization Scale, Causes of Obesity Questionnaire, International Physical Activity Questionnaire, and the Sedentary Behavior Questionnaire. \nResults: WBI scores (3.38 ± 1.58) were higher among females and individuals with higher BMIs (p < 0.001 for all). Participants mainly endorsed behavioural causes of obesity. WBI was associated with more weekly hours of sedentary behaviour (B = 0.85, p < .001). Explicit weight bias was associated with more weekly minutes of vigorous physical activity (B = 12.87, p < .05). \nConclusions: This study highlights WBI as a problem that is associated with adverse health behaviours among all individuals across the weight spectrum. Future research should investigate the longitudinal impact of weight bias on health behaviours.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".