A Philosophical Approach to Addressing Uncertainty in Medical Education
Bibliographic record
Abstract
Conveying the uncertainty inherent in clinical practice has rightly become a focus of medical training. To date, much of the emphasis aims to encourage trainees to acknowledge and accept uncertainty. Intolerance of uncertainty is associated with medical student distress and a tendency in clinicians toward overtreatment. The authors argue that a deeper, philosophical understanding of the nature of uncertainty would allow students and clinicians to move beyond simple acceptance to explicating and mitigating uncertainty in practice.Uncertainty in clinical medicine can be categorized philosophically as moral, metaphysical, and epistemic uncertainty. Philosophers of medicine-in a way analogous to ethicists a half century ago-can be brought into medical education and medical practice to help students and physicians explore the epistemic and metaphysical roots of clinical uncertainty. Such an approach does not require medical students to master philosophy and should not involve adding new course work to an already-crowded medical curriculum. Rather, the goal is to provide students with the language and reasoning skills to recognize, evaluate, and mitigate uncertainty as it arises. The authors suggest ways in which philosophical concepts can be introduced in a practical fashion into a variety of currently existing educational formats. Bringing the philosophy of medicine into medical education promises not only to improve the training of physicians but, ultimately, to lead to more mindful clinical practice, to the benefit of physicians and patients alike.
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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 | 0.023 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".