Exploring context and culture in clinical reasoning medical education: A qualitative exploratory study
Bibliographic record
Abstract
BACKGROUND: Clinical reasoning processes are complex and interwoven with culture and context. While these relationships have been explored to understand the outcomes of clinical reasoning, there has been little exploration of how to integrate these relationships when teaching and learning clinical reasoning. METHODS: Using semi-structured interviews, this research explored the role of context and culture in clinical reasoning medical education. Participants were clinical teachers recruited from across Northern Ontario. The data were analysed independently by two reviewers using both thematic analysis and critical discourse analysis, and peer reviewed by a third researcher. RESULTS: The role of context and culture is inherent to the personal, professional and pedagogical aspects of clinical reasoning, especially when teaching about the complexities of Northern Ontario. The major themes that came through were: 1) teaching and learning clinical reasoning needs reflexivity, 2) developing clinical reasoning skills needs time and 3) clinical reasoning pedagogy should acknowledge and encompass practice variation and patient diversity. CONCLUSION: Teaching clinical reasoning in Northern Ontario involves being aware of the complexities that are inherent in interacting with patients and communities. Through personal, professional and pedagogical models, the students and teachers can address the complexities of cultural and contextual clinical reasoning.
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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 | 0.018 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| 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".