Anti-Fat Attitudes Towards Weight Gain Caused by the COVID-19 Pandemic or by “Unhealthy” Lifestyle Choices
Bibliographic record
Abstract
Given the ubiquity of anti-fat prejudice, in this experimental study, we tested whether weight gain attributed to COVID-19 would influence evaluations of overweight male and female targets. Female participants (N = 160) were randomly assigned to read one of four mock medical forms that outlined distractor medical information (e.g., blood requisition results), the sex of the target (male vs. female) and stated reason for weight gain (unhealthy lifestyle choices vs. inactivity due to the COVID-19 lockdown). Participants evaluated the patient on a series of binary adjectives (e.g., lazy/industrious), and completed measures assessing anti-fat attitudes (i.e., fear of becoming fat and belief in the controllability of weight), internalization of ideal standards of appearance, and BMI (i.e., self-reported weight and height). Contrary to our predictions, we found that overweight male and female patients were evaluated similarly regardless of whether their weight gain was attributable to unhealthy lifestyle choices or inactivity due to the COVID-19 pandemic. Finally, believing that one’s weight is controllable and internalizing general standards of attractiveness correlated positively with fat disparagement of the medical patients. Participants’ BMI and fear of fat, however, were negligibly related to fat disparagement. Possible explanations for our findings, implications for healthcare settings, and directions for future research are explored.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".