Accounting for social accountability: Developing critiques of social accountability within medical education
Bibliographic record
Abstract
BACKGROUND: The concept of the social accountability of medical schools has garnered many followers, in response to a broad desire for greater social justice in health care. As its use has spread, the term 'social accountability' has become a meta-narrative for social justice and an inevitable and unquestionable good, while at the same time becoming increasingly ambiguous in its meaning and intent. In this article, we use the lenses of postmodernism and critical reflexivity to unpack the multiple meanings of social accountability. In our view, subjecting the concept of 'social accountability' to critique will enhance the ability to appraise the ways in which it is understood and enacted. DISCUSSION: We contend that critical reflexivity is necessary for social accountability to achieve its aspirations, and hence we must be prepared to become accountable not only for our actions, but also for the ideologies and discourses underlying them.
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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 | 0.110 | 0.154 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.019 | 0.140 |
| Scholarly communication | 0.024 | 0.033 |
| Open science | 0.005 | 0.017 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.003 | 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".