Five ways to counter ableist messaging in medical education in the context of promoting healthy movement behaviours
Bibliographic record
Abstract
One in five Canadians have a disability and there are well-documented gaps in care for this equity-deserving group that have roots in medical education. In this paper, we highlight the unintended consequences of ableist messaging for persons living with disabilities, particularly in the context of promoting healthy movement behaviours. With its broad reach and public trust, the medical community has a responsibility to acknowledge the reality of ableism and take meaningful action. We propose five strategies to counter ableist messaging in medical education: (1) increase knowledge and confidence among physicians and trainees to optimize movement behaviours in persons living with disabilities, (2) perform personal and institutional language audits to ensure terminology related to disability is inclusive and avoids causing unintended harm, (3) challenge ableist messages effectively, (4) address the unmet healthcare needs of persons living with disabilities, and (5) engage in efforts to reform medical curricula so that persons living with disabilities are represented and treated equitably. Physicians and trainees are well-positioned to deliver competent and inclusive care, making medical education an opportune setting to address health inequities related to disability.
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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 | 0.085 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.020 | 0.017 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.007 | 0.018 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 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".