Weight Prejudice and Medical Policy: Support for an Ambiguously Discriminatory Policy Is Influenced by Prejudice‐Colored Glasses
Bibliographic record
Abstract
This study examined the influence of affectively‐based weight prejudice versus weight control beliefs on perceptions of and support for an ambiguously discriminatory medical policy: denying surgery to overweight patients. Participants read a news article describing a new policy in the United Kingdom of denying surgery to overweight patients, and reported their reactions to the policy. Results revealed that participants who scored higher on an affectively‐based measure of weight prejudice that was completed 3–4 weeks before the main session were less likely to perceive the medical policy as discriminatory, more likely to agree with the policy and to support adoption of a similar policy in their own country, and recommended lower body mass index (BMI) cutoff values for denying surgery to overweight patients, whereas weight control beliefs had less of a role to play. In addition, perceptions of the policy as (non)discriminatory mediated the effects of weight prejudice on policy agreement, support, and recommended BMI cutoff. These results indicate that affective prejudice influences individuals' support for an ambiguously discriminatory medical policy, which has important implications for policy makers and researchers.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".