The Education and Implementation of Clinical Reasoning Education in Post-Graduate Emergency Medicine Training: An Integrative Review
Bibliographic record
Abstract
The ability to clinically reason is a necessity for doctors to reduce the possibility of diagnostic error in patients. Emergency medicine doctors work in a highly active, challenging and, at times, cognitively formidable setting that can affect clinical reasoning. This review aims to study the literature on post-graduate training programs and how clinical reasoning education is incorporated. Methods: CINAHL, Embase and Medline were used to obtain relevant literature from 2010 to 2023. A review was undertaken of clinical reasoning curriculums and resources in medical and surgical specialties in Australia, New Zealand, USA, UK, Ireland and Canada. Results: 23 articles were included in the review. Educational themes to teach clinical reasoning were a) simulation b) technological and innovative methods, c) morning report, d) peer teaching, and e) curriculum related. Surgery, General practice, and Internal Medicine colleges appear to have embedded clinical reasoning courses and resources within their curriculum and training. Conclusion: Specialty colleges are recognising the importance of clinical reasoning and have incorporated formal curricula into their training. Teaching can occur at a college, hospital and departmental level using a variety of educational modalities that vary in the resources required and is a possible option for Emergency Medicine training.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".