What is so difficult about managing clinical reasoning difficulties?
Bibliographic record
Abstract
CONTEXT: Clinical reasoning is the cornerstone of medical competence. Difficulties in this area are often identified late in clinical training. Studies point to challenges faced by clinical educators in their dual roles as clinicians and educators. Little is known about the common, yet complex, issue of how they manage clinical reasoning difficulties. We therefore sought to: (i) describe the current state of affairs in various clinical teaching settings, and (ii) explore the factors that determine the behaviour of clinical educators in this respect. METHODS: Four focus groups were conducted with 26 clinical educators in general practice, internal medicine and emergency medicine in Belgium and Switzerland. Two researchers analysed the transcripts of the focus group discussions using Fishbein's integrative model of behaviour prediction in a theory-driven, immersion-crystallisation process. Experienced faculty members validated the findings. RESULTS: Across diverse settings, the process of identifying and remediating clinical reasoning difficulties was unstructured. Consistent with Fishbein's model, clinical educators' underlying beliefs determined their behaviour. They believed in the apprenticeship model of learning in the clinical environment, in which their educational role was limited to role-modelling and in which residents were responsible for assimilating skills. They were sceptical about the potential impact of remediation. A few more knowledgeable supervisors had a stronger sense of their educational role, but did not implement systematic procedures to manage clinical reasoning difficulties. Environmental constraints were symptomatic of a collective paradigm of residency as an apprenticeship, in which the focus is on clinical duties, rather than as an educational programme. CONCLUSIONS: In order to improve the current state of affairs in the management of clinical reasoning difficulties, a collective paradigm shift is required to alter the perception of residency as an apprenticeship to one of residency as a structured educational programme. Faculty development programmes should be designed in an integrated way so that they not only develop clinical educators' skills, but also modify their beliefs.
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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 | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".