Promoting epistemic justice: Supporting inclusion and belonging for disabled individuals in health professions
Bibliographic record
Abstract
BACKGROUND: The underrepresentation of students and professionals with disabilities in health professions is well-documented in research, emphasizing the urgent need for greater inclusivity. Institutional structures often restrict disabled individuals from sharing their specialized knowledge on navigating disability, perpetuating epistemic injustice. Research emphasizes the importance of amplifying their voices to address inequities and restore epistemic justice. OBJECTIVES: This study explores the firsthand, experiential views of the challenges and supporting factors that disabled students and professionals face in the health professions education and practice. Participants provided advice for their disabled peers and non-disabled allies. The development of a critical disability epistemology amplifies underrepresented voices in the health field. DESIGN: This qualitative study was guided by a constructivist approach, with data analysis informed by reflective thematic analysis. METHODS: A series of semi-structured interviews were conducted with 56 participants (27 students and 29 professionals) in nursing, medicine, occupational therapy, physiotherapy, and social work. Participants were interviewed up to three times over the course of a year, resulting in a total of 124 interviews. RESULTS: Two main categories were identified. Category one, advice for disabled students and professionals, includes the themes: (1) Negotiating disclosure processes to mobilize support, (2) Recognizing personal boundaries and strengths while actively seeking mentorship, and (3) Advocating for oneself and others. Category two, advice for non-disabled allies, encompasses the themes: (4) Fostering inclusivity through thoughtful language, education, and support, and (5) Actively promoting systemic change. CONCLUSION: The findings enhance the epistemic agency of disabled individuals by utilizing community resources for collective knowledge production. They offer valuable guidance for educators, institutions, and policymakers, providing a roadmap for making health education programs and workplaces more inclusive and supportive for disabled individuals.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
| gpt | no category Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | low |
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 | 0.028 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.025 | 0.034 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.003 | 0.047 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".