Implicit weight bias exists among veterinary professionals
Bibliographic record
Abstract
Objective: To explore implicit weight bias and weight stigma by association within a sample of veterinary professionals. Methods: An electronic survey was administered in person to veterinary professionals. Participants were presented with 1 of 8 possible scenarios including silhouettes of pets (4 cats, 4 dogs) and clients with varying combinations of weight statuses. Participants were asked questions rating their perception of the clients' capacity as pet caregivers. Participants completed the validated Implicit Association Test for weight. Logistic regression was conducted to detect differences in veterinary professionals' perceptions of the pets and clients based on displayed weight status. Results: 138 veterinary professionals participated; the majority (56.0%) were registered veterinary technicians. Most participants (70.3%) reported having pet-weight-related conversations with clients either daily or multiple times a week. Participants rated owners of overweight dogs as less effective caregivers than owners of lean dogs (OR, 0.29; 95% CI, 0.11 to 0.77). Participants rated owners of overweight cats as more caring than owners of lean cats (OR, 2.89; 95% CI, 1.02 to 8.16). The Implicit Association Test indicated that the majority (90.6%) of participants had some level of unconscious preference for people who were lean over people who were overweight. Conclusions: Veterinary professionals' perceptions of dog and cat owners based on their pet's weight may differ across species. Findings potentially represent implicit weight bias among veterinary professionals that warrants further research. Clinical Relevance: When interacting with clients owning an overweight or obese pet, veterinary professionals should be aware that they may hold a weight bias.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".