Evolution of attitudes toward people with disabilities in healthcare practitioners and other occupations from 2006 to 2024
Bibliographic record
Abstract
ABSTRACT Purpose Healthcare practitioners have shown implicit and explicit attitudes that disfavor people with disabilities. This study aimed to describe how these attitudes evolved between 2006 and 2024 across clinicians, rehabilitation assistants, and other occupations. Methods In this comparative repeated cross-sectional study, data from 660,430 participants from Project Implicit were analyzed. Implicit attitudes were assessed using D-scores derived from the Disability Implicit Association Test. Explicit attitudes were assessed using a Likert scale. Generalized additive models were conducted to test the evolution of attitudes over time. Results Explicit attitudes toward people with disabilities became less unfavorable over time, following a linear pattern. No such effect was found for implicit attitudes. However, non-linear interactions between time, occupation group, and sex suggest a complex effect of time on attitudes that should be interpreted in the context of each specific combination of occupation group and sex, rather than assuming a uniform trend. Attitudes were less favorable toward people with physical disabilities than general disabilities. Conclusions The contrast between evolution of implicit and explicit attitudes suggests that implicit bias remains resistant to change despite improving explicit consideration of people with disabilities. Knowledge of these patterns may inform training programs to reduce bias in healthcare and beyond.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".