Ableism within health care professions: a systematic review of the experiences and impact of discrimination against health care providers with disabilities
Bibliographic record
Abstract
PURPOSE: People with disabilities experience concerning rates of ableism and are significantly under-represented within healthcare education and professions. Focusing on healthcare professions is important for enhancing the diversity and inclusion of equity-deserving groups within the workforce. The objective of this review was to explore the experiences and impact of workplace discrimination and ableism among healthcare providers and trainees with disabilities. METHODS: Systematic searches of seven databases from 2000 to January 2022 were conducted. Five reviewers independently applied the inclusion criteria, extracted the data and rated the study quality. RESULTS: 48 studies met our inclusion criteria, representing 13,815 participants across six countries over a 21-year period. The findings highlighted rates and types of workplace ableism, which occurred at the institutional (i.e., inaccessible environments, physical barriers and unsupportive work environments) and individual level (i.e., negative attitudes, bullying, harassment). The impact of ableism on healthcare providers included difficulty disclosing due to fear of stigma, and effects on well-being and career development. CONCLUSIONS: Our findings revealed a critical need for more research on the experiences of ableism amongst healthcare providers and the impact it has on their well-being. Further efforts should explore mechanisms for including and welcoming people with disabilities in healthcare professions.Implications for rehabilitationWorkplace ableism is prevalent in health care professions and could be discouraging people with disabilities from entering or completing health care education and training, leading to an under-representation of this equity-deserving group within health care.More efforts are needed to recruit, retain and support people with disabilities in the health care workforce.Health care providers who have a disability often experience workplace discrimination and inaccessible physical environments which can impact their health and well-being.Managers, senior leadership and health care organizations should advocate for improved social inclusion of employees with disabilities.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".