Understanding cultural dynamics shaping clinical reasoning skills: A dialogical exploration
Bibliographic record
Abstract
Our study examined the influence of national cultural predispositions on training medical professionals and doctor-patient dynamics using a dialogical approach, guided by Hofstede's framework. This framework provided valuable insights into how cultural tendencies shape the learning and application of clinical reasoning skills in different cultural contexts. We found that dimensions such as power distance and individualism versus collectivism significantly influenced clinical reasoning, while other dimensions had more nuanced effects. Junior doctors in Southern nations, despite initially lagging behind, developed advanced clinical reasoning skills with experience, eventually matching their Northern counterparts. The study highlighted the link between cultural norms and educational practices, variations in family involvement during reasoning, adherence to clinical guidelines and doctors' emotional engagement in clinical care between Southern and Northern contexts. Additionally, we recognised that effective clinical reasoning extends beyond technical knowledge, involving an understanding and integration of cultural dynamics into patient care. This highlights the pressing need to prioritise this topic.
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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 | 0.017 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".