Predictors of support for anti-weight discrimination policies among Canadian adults
Bibliographic record
Abstract
Introduction: Weight discrimination of individuals with overweight or obesity is associated with adverse mental and physical health. Weight discrimination is prevalent in many sectors such as within workplaces, where individuals with overweight and obesity are denied the same opportunities as individuals with lower weight status, regardless of performance or experience. The purpose of this study was to understand the Canadian public's support or opposition of anti-weight discrimination policies and predictors of support. It was hypothesized that Canadians will show support of anti-weight discrimination policies to some extent. Methods: = 923, 50.76% women, 74.4% White) who responded to an online survey assessing weight bias and support of twelve anti-weight discrimination policies related to societal policies (e.g., implementing laws preventing weight discrimination) and employment-related policies (e.g., making it illegal to not hire someone due to their weight). Participants completed the Causes of Obesity Questionnaire (COB), the Anti-Fat Attitudes Questionnaire (AFA) and the Modified Weight Bias Internalization Scale (WBIS-M). Multiple logistic regressions were used to determine predictors of policy support. Results: Support for policies ranged from 31.3% to 76.9%, with employment anti-discrimination policies obtaining greater support than societal policies. Identifying as White and a woman, being over the age of 45 and having a higher BMI were associated with an increased likelihood of supporting anti-weight discrimination policies. There were no differences between the level of support associated with attributing obesity to behavioral or non-behavioral causes. Explicit weight bias was associated with a reduced likelihood of supporting 8/12 policies. Weight Bias Internalization was associated with an increased likelihood of supporting all societal policies but none of the employment policies. Conclusions: Support for anti-weight discrimination policies exists among Canadian adults, and explicit weight bias is associated with a lower likelihood of supporting these policies. These results highlight the need for education on the prevalence and perils of weight discrimination which may urge policy makers to consider weight bias as a form of discrimination that must be addressed. More research on potential implementation of anti-weight discrimination policies in Canada is warranted.
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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.001 |
| 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".