Disability competencies for disability rights in the curriculum in the Global North and Global South
Bibliographic record
Abstract
This chapter brings us up-to-date with several international research case studies that have critically analyzed the incorporation of disability rights into health professions education in the Group of Twenty nations including both the Global North and the Global South (the United States, Canada, Australia, India, China, and Mexico). Each of the studies shares certain characteristics. First and foremost, this body of work is informed by the Convention on the Rights of Persons with Disabilities and the World Report on Disability. Secondly, each study respects the dictum “Nothing about, us without us” and thirdly, each study focuses on how to make progress in incorporating disability rights into curricula through a competency-based approach. Methodologically the studies reveal the nature and extent of progress towards the achievement of disability competencies as a rights-based approach towards working with people with disabilities. Importantly each of the studies included people with disabilities in their study design and conduct. The chapter reveals there is considerable scope for the health professions to strengthen human rights-based education and care provision through ethical codes of conduct, competency-based education, curriculum renewal, accreditation, and registration requirements. Finally, the chapter brings to light barriers and enablers in making progress towards realizing disability rights as human rights in health professional education in the future across the Globe.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".