Seeking impact of medical schools on health: meeting the challenges of social accountability
Bibliographic record
Abstract
CONTEXT: The acquisition of sums of knowledge and mastery of sophisticated technologies by medical graduates is insufficient for their responsibilities to recognise and adapt to people's evolving needs. RESPONSE: A Global Consensus on Social Accountability for Medical Schools brought together 130 organisations and individuals from around the world with responsibility for health education, professional regulation and policy making to participate for 8 months in a three-round Delphi process leading to a 3-day consensus development conference which included weighted representation from all regions of the world. The resulting Consensus reflects agreement on 10 strategic directions to enable a medical school to be socially accountable. RESULTS: The list of 10 directions embraces a system-wide scope from identification of health needs to verification of the effects of medical schools on those needs, all driven by the quest for positive impact on peoples' health status. This includes an understanding of the social context, an identification of health challenges and needs and the creation of relationships to act efficiently (directions 1 and 2). Within the spectrum of the health workforce required to address health needs, the anticipated role and competences of the doctor are described (direction 3) serving as a guide to the education strategy (direction 4), which the medical school is called to implement along with consistent research and service strategies (direction 5). Standards are required to steer the institution towards a high level of excellence (directions 6 and 7), which national authorities need to recognise (direction 8). While social accountability is a universal value (direction 9), local societies will be the ultimate appraisers of the achievements of the school and its graduates (direction 10).
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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 | 0.197 | 0.212 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.021 | 0.045 |
| Scholarly communication | 0.028 | 0.026 |
| Open science | 0.004 | 0.063 |
| Research integrity | 0.013 | 0.016 |
| Insufficient payload (model declined to judge) | 0.009 | 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".