Implicit and explicit attitudes toward people with physical disabilities among clinicians, rehabilitation assistants, and other occupations: A comparative study
Bibliographic record
Abstract
ABSTRACT Objective Ableism is rooted in attitudes that influence behaviors and decisions toward people with disabilities. To assess whether these attitudes vary by occupation, we compared the preferences for people with or without physical disabilities between clinicians, rehabilitation assistants, and individuals in other professions. Methods Data from 213,191 participants, collected online through Project Implicit, were analyzed, including 6445 clinicians, 3482 rehabilitation assistants, and 203,264 individuals in other occupations. Implicit attitudes were assessed using D-scores derived from the Implicit Association Test. Explicit attitudes were assessed using a Likert scale. Regression models were conducted to examine the association between occupation groups and attitudes toward people with and without physical disabilities, while controlling for demographic variables. Results Clinicians and rehabilitation assistants showed both an implicit and explicit preference for people without physical disabilities. Implicit attitudes of clinicians and rehabilitation assistants were equivalent to those in other occupations. Compared to other occupations, clinicians had less favorable explicit attitudes toward people with physical disabilities, whereas rehabilitation assistants had more favorable ones. Older age, male sex, and no personal experience of disability were associated with less favorable attitudes toward people with physical disabilities. Associations with education, race, geographic region, and year of data collection were also observed. Conclusions This study provides evidence suggesting the persistence of ableist attitudes among clinicians and rehabilitation assistants. Moreover, implicit attitudes were similar to those of other occupations, while explicit attitudes of clinicians were even slightly less favorable. Impact Our findings suggest that despite ongoing educational shifts toward more inclusive approaches, the longstanding framework of disability as an abnormality to be normalized may still affect healthcare practitioners. This underscores the need for continued efforts to address ableism not only in healthcare, but throughout society, by promoting disability-inclusive education and training.
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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 | 0.007 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".