Barriers for the Inclusion of Clinicians Living with Disabilities in Healthcare Professions: The Peers’ Perspectives
Bibliographic record
Abstract
Background Health professionals living with disabilities are valuable to healthcare due to their dual identity as a clinician and a person living with disability yet remain underrepresented in the workforce. While the barriers to inclusion of clinicians living with disabilities from their own perspective are reported, little is known about peers’ perspectives. Objective This study aims to explore the barriers and gaps in knowledge to the inclusion of clinicians living with disabilities from the perspective of colleagues. Methods Health professionals and staff (healthcare workers) in a public health authority in Canada responded to a survey consisting of closed and open questions, under 3 subthemes: attitudes, institutional barriers, and gaps in knowledge. Inferential statistics were performed to assess the overall attitudes and differences in perceptions based on types of disability. Results Attitudes and barriers varied based on type of disability - clinicians living with physical disabilities were perceived most positively compared to those living with cognitive/learning disabilities. Attitudes also varied based on experience with disability - clinicians with lived experience had more positive attitudes. Gaps in knowledge were found to exist on an individual and organizational level. Conclusion The results of this study can open the discussion for rehabilitation and human resource professionals needing to learn about workplace accommodations for health professionals living with disabilities and support addressing attitudes and barriers they face.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".