What's your experience?: A duoethnographic dialogue to advance disability inclusion in medical education
Bibliographic record
Abstract
BACKGROUND: Although disability inclusion in medical education is gaining interest internationally, scholarship and policy recommendations on this topic largely hail from the US, Canada, Australia and the UK. Existing scholarship, while calling for medical education to enact cultural and attitudinal change related to disability, has yet to exemplify how educators might critically examine their understandings. APPROACH: As two medical educators and researchers, one based in New Zealand and the other based in Saudi Arabia, we took a duoethnographic approach to explore tensions, possibilities and assumptions regarding disability and disability inclusion in medical education. Through a year-long synchronous and asynchronous dialogue, we examined our experiences in relation to literature from critical disability studies and disability inclusion in medical education. FINDINGS: We present recurrent themes from our dialogue. We consider what disability means, explore definitions and models of disability in our contexts, as well as our lived curriculum of disability. We grapple with the applicability of disability inclusion practices across borders. We explore the complexity of supporting access without a clear roadmap, while recognising educators' potential in this work. Finally, we recognise that, if disability is relational, we have the power and responsibility to address ableism in medical education. Throughout, we return to the importance of local consultation with disabled people (learners, physicians) to better understand how services ought to be oriented. CONCLUSION: Duoethnographic dialogue is a fruitful approach to critically examine understandings of disability with others and represents a necessary start to work in education that seeks to advance justice. We share possible actions to take the work forward beyond dialogue and suggest that readers engage in such dialogues with others in their own contexts.
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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 | 0.034 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.042 | 0.072 |
| Scholarly communication | 0.022 | 0.031 |
| Open science | 0.003 | 0.033 |
| Research integrity | 0.011 | 0.028 |
| Insufficient payload (model declined to judge) | 0.004 | 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, 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".