It Is Time to Address Ableism in Academia: A Systematic Review of the Experiences and Impact of Ableism among Faculty and Staff
Bibliographic record
Abstract
Faculty and staff with disabilities are significantly underrepresented within academia and experience alarming rates of discrimination, social exclusion and marginalization. This review aimed to understand the experiences and impact of disability discrimination (ableism) among faculty and staff. We conducted a systematic review while searching six international databases that identified 33 studies meeting our inclusion criteria. Of the 33 studies that were included in our review, they involved 1996 participants across six countries, over a 25-year period. The studies highlighted faculty and staff experiences of ableism in academia, which focused on disclosure (i.e., choosing to disclose or not), accommodations (i.e., lack of workplace accommodations and the difficult process for obtaining them) and negative attitudes (i.e., stigma, ableism and exclusion). Twenty-one studies explained the impact of ableism in academia, including a negative effect on physical and mental health, and career development. Coping mechanisms and strategies to address ableism in academia were also described. There is a critical need for more research and attention to the lived experiences of ableism among faculty and staff in academia and the impact that ableism has on their health and well-being.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".