The Social Accountability of Medical Schools and its Indicators
Bibliographic record
Abstract
CONTEXT: There is growing interest worldwide in social accountability for medical and other health professional schools. Attempts have been made to apply the concept primarily to educational reform initiatives with limited concern towards transforming an entire institution to commit and assess its education, research and service delivery missions to better meet priority health needs in society for an efficient, equitable an sustainable health system. METHODS: In this paper, we clarify the concept of social accountability in relation to responsibility and responsiveness by providing practical examples of its application; and we expand on a previously described conceptual model of social accountability (the CPU model), by further delineating the parameters composing the model and providing examples on how to translate them into meaningful indicators. DISCUSSION: The clarification of concepts of social responsibility, responsiveness and accountability and the examples provided in designing indicators may help medical schools and other health professional schools in crafting their own benchmarks to assess progress towards social accountability within the context of their particular environment.
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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 | 0.043 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".