Social accountability: The extra leap to excellence for educational institutions
Bibliographic record
Abstract
More than ever are we facing the challenge of providing evidence that what we do responds to priority health needs and challenges of the ones we intend to serve: patients, citizens, families, communities and the nation at large. Which are those health needs and challenges? Who defines them? How do medical schools organize themselves to address them through their education, research and service delivery functions? Principles of social accountability call for an explicit three-tier engagement: identification of current and prospective social needs and challenges, adaptation of school's programmes to meet them and verification that anticipated effects have benefited society. Measurement tools need to be designed and tested to steer development in this direction, particularly to establish a meaningful relationship between inputs, processes, outputs and impact on health. The Global Consensus on Social Accountability of Medical Schools provides a unique opportunity to foster collaborative research and development in an area of great significance for the future of medical education.
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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 | 0.126 | 0.141 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.035 |
| Scholarly communication | 0.032 | 0.035 |
| Open science | 0.004 | 0.039 |
| Research integrity | 0.019 | 0.021 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".