Direct Observation Tools in Emergency Medicine: A Systematic Review of the Literature
Bibliographic record
Abstract
Objectives: Direct observation is important for assessing the competency of medical learners. Multiple tools have been described in other fields, although the degree of emergency medicine-specific literature is unclear. This review sought to summarize the current literature on direct observation tools in the emergency department (ED) setting. Methods: We searched PubMed, Scopus, CINAHL, the Cochrane Central Register of Clinical Trials, the Cochrane Database of Systematic Reviews, ERIC, PsycINFO, and Google Scholar from 2012 to 2020 for publications on direct observation tools in the ED setting. Data were dual extracted into a predefined worksheet, and quality analysis was performed using the Medical Education Research Study Quality Instrument. Results: We identified 38 publications, comprising 2,977 learners. Fifteen different tools were described. The most commonly assessed tools included the Milestones (nine studies), Observed Structured Clinical Exercises (seven studies), the McMaster Modular Assessment Program (six studies), Queen's Simulation Assessment Test (five studies), and the mini-Clinical Evaluation Exercise (four studies). Most of the studies were performed in a single institution, and there were limited validity or reliability assessments reported. Conclusions: The number of publications on direct observation tools for the ED setting has markedly increased. However, there remains a need for stronger internal and external validity data.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".