Acting wisely in complex clinical situations: ‘Mutual safety’ for clinicians as well as patients
Bibliographic record
Abstract
PURPOSE: The hope that reliably testing clinicians' competencies would improve patient safety is unfulfilled and clinicians' psychosocial safety is deteriorating. Our purpose was to conceptualise 'mutual safety', which could increase benefit as well as reduce harm. METHODS: A cultural-historical analysis of how medical education has positioned the patient as an object of benefit guided implementation research into how mutual safety could be achieved. RESULTS: Educating doctors to abide by moral principles and use rigorous habits of mind and scientific technologies made medicine a profession. Doctors' complex attributes addressed patients' complex diseases and personal circumstances, from which doctors benefited too. The patient safety movement drove reforms, which reorientated medical education from complexity to simplicity: clinicians' competencies should be standardised and measurable, and clinicians whose 'incompetence' caused harm remediated. Applying simple standards to an increasingly complex, and therefore inescapably risky, practice could, however, explain clinicians' declining psychosocial health. We conducted a formative intervention to examine how 'acting wisely' could help clinicians benefit patients amidst complexity. We chose the everyday task of insulin therapy, where benefit and harm are precariously balanced. 247 students, doctors, and pharmacists used a thought tool to plan how best to perform this risky task, given their current clinical capabilities, and in the sometimes-hostile clinical milieus where they practised. Analysis of 1000 commitments to behaviour change and 600 learning points showed that addressing complexity called for a skills-set that defied standardisation. Clinicians gained confidence, intrinsic motivation, satisfaction, capability, and a sense of legitimacy from finding new ways of benefiting patients. CONCLUSION: Medical education needs urgently to acknowledge the complexity of practice and synergise doctors' and patients' safety. We have shown how this is possible.
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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 | 0.051 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".