Bibliographic record
Abstract
In this paper, we explore the recent emphasis, in various medical contexts, of the term 'critical' or the notion of 'being critical'. We identify various definitions of being critical and note that they differ strikingly. What are these different uses of the term trying to capture that is important in clinical medicine and medical education? We have analysed these qualities as responsibilist, epistemic virtues. We believe that a virtues approach is best able to make sense of the non-cognitive elements of 'being critical', such as the honesty and courage to question claims in the face of persuasion, authority or social pressure. Medical educators and professional bodies seem to agree that being critical is important and desirable. Yet, it is unclear how this quality can be optimally fostered and balanced with the constraints that act upon individual practitioners in the context of institutional medicine including professional standards and the demands of the doctor-patient relationship. Other constraints such as authoritarianism, intimidation and financial pressures may act against the expression of being critical or even the cultivation of critical thinking. The issue of the constraints on critical thinking and the potential hazards it entails will require further consideration by those who encourage being critical in medicine.
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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 | 0.075 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.010 | 0.106 |
| Scholarly communication | 0.034 | 0.037 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.014 | 0.029 |
| Insufficient payload (model declined to judge) | 0.003 | 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".