Evolution of Attitudes Toward People with Disabilities Among Health Care Practitioners and Other Workers, 2006–2024
Bibliographic record
Abstract
Purpose: Health care practitioners have shown implicit and explicit attitudes that disfavour people with disabilities. I describe how these attitudes evolved between 2006 and 2024 across clinicians, rehabilitation assistants, and individuals in other occupations. Method: I analyzed data from 660,430 participants from Project Implicit. Implicit attitudes were assessed with Disability Implicit Association Test (IAT) D-score. Explicit attitudes were assessed using a Likert scale. I conducted generalized additive models to test the evolution of attitudes over time. Results: Explicit attitudes toward people with disabilities became less unfavourable over the 19 years of the study, irrespective of occupation. This effect was not observed 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. Additionally, attitudes were more unfavourable toward people whose disability was physical. Conclusions: The contrast between the evolution of implicit attitudes and that of explicit attitudes suggests that implicit bias remains resistant to change despite improving explicit consideration of disabled people. Knowledge of these patterns may inform training programmes to reduce bias in health care and beyond.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".