Bibliographic record
Abstract
Objective: There has been an observable trend towards developing medical competency frameworks across the globe. These competency frameworks are intended to improve societal trust in the medical education system in developing appropriately competent medical practitioners. A framework developed by the Royal College of Physicians and Surgeons; Canada has been widely accepted by several institutions across the world. Medical Council of India has also published a similar framework of medical competencies. Most of these frameworks does not consider ancient Indian frameworks which have existed for several thousand years. Current paper examines the medical competency frameworks from ancient India and compares it with current frameworks. Method: A review of literature available in reputable libraries and online on the medical competency framework from ancient India has been attempted. Key words including 'competency framework, medical framework, ancient India and fully equipped physician' were used. Results: A medical competency framework was written and implemented more than two thousand years ago. The framework identified key competencies including: Medical expertise, Communication skills, Scholar, Health advocacy and Professionalism. This framework was used for medical practitioners at the time and used during the training and subsequent medical practice. Conclusion: There is striking similarity between ancient Indian and current model of competency framework. Teachings and wisdom from ancient India can prove invaluable while developing future medical competency frameworks.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".