Setting the Educational Agenda and Curriculum for Error Prevention in Emergency Medicine
Bibliographic record
Abstract
Graduate and postgraduate medical education currently teaches safety in patient care by instilling a deep sense of personal responsibility in student practitioners. To increase safety, medical education will have to begin to introduce new concepts from the "safety sciences," without losing the advantages that the values of commitment and responsibility have gained. There are two related educational goals. First, we in emergency medicine (EM) must develop a group of safety-educated practitioners who can understand and implement safe practice innovations in their clinical settings, and will be instrumental in changing our professional culture. Second, EM must develop a group of teachers and researchers who can begin to deeply understand how safety is maintained in emergency care, develop solutions that will work in emergency department settings, and pass on those insights and innovations. The specifics of what should be taught are outlined briefly. Work is currently ongoing to identify more specifically the core content that should be included in educational programs on patient safety in emergency care. Finally, careful attention will have to be paid to the way in which these principles are taught. It seems unlikely that a series of readings and didactic lectures alone will be effective. The analysis of meaningful cases, perhaps supplemented by high-fidelity simulation, seems to hold promise for more successful education in patient safety.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".