Disability-inclusive compassionate care: Disability competencies for an Indian Medical Graduate
Bibliographic record
Abstract
The new curriculum of the Medical Council of India (MCI) lacks disability-related competencies. This further involves the risk of perpetuating the medicalization of diverse human experiences and many medical students may graduate with little to no exposure to the principles of disability-inclusive compassionate care. Taking into consideration the UN Convention, the Rights of Persons with Disabilities, Act 2016, and by involving the three key stakeholders - disability rights activists, doctors with disabilities, and health profession educators - in the focus group discussions, 52 disability competencies were framed under the five roles of an Indian Medical Graduate (IMG) as prescribed by the MCI. Based on feedback from other stakeholders all over India, the competencies were further refined into 27 disability competencies (clinician: 9; leader: 4; communicator: 5; lifelong learner: 5; and professional: 4) which the stakeholders felt should be demonstrated by health professionals while they care for patients with disabilities. The competencies are based on the human rights approach to disability and are also aligned with the competencies defined by accreditation boards in the US and in Canada. The paper describes the approach used in the framing of these competencies, and how parts of these were ultimately included in the new competency-based medical education curriculum in India.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.006 |
| 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".