SPIRALS: An Approach to Non-Linear Thinking for Medical Students in the Emergency Department
Bibliographic record
Abstract
Context We lack guidelines to inform the necessary components of an emergency medicine undergraduate rotation. Traditionally, clinical reasoning has been taught using linear thought processes likely not ideal for diagnostic and management decisions made in the emergency department. Methods We used the Delphi method to obtain consensus on a set of competencies for undergraduate emergency medicine that illustrate the non-linear concepts we believe are necessary for learners. Competencies were informed by a naturalistic observational study of emergency physicians. A survey outlining these competencies was subsequently circulated to emergency physicians who rated their relative importance. Results Eleven competencies were included in Round 1, all rated within the "for consideration" for inclusion range. This was reduced to 10 competencies in Round 2, which was only marginally more definitive with respondents rating one competency in the "definite inclusion range" and the remaining in the "for consideration" range. Conclusions This study was conducted to address a gap in the current undergraduate emergency medicine curriculum. Consensus on the relative importance of each competency was not achieved, though we believe that the competencies that arose from this study will help medical students develop the non-linear thinking processes necessary to succeed in emergency medicine.
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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 | 0.041 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".