Assessment of emergency medicine residents: a systematic review
Bibliographic record
Abstract
Background: Competency-based medical education is becoming the new standard for residency programs, including Emergency Medicine (EM). To inform programmatic restructuring, guide resources and identify gaps in publication, we reviewed the published literature on types and frequency of resident assessment. Methods: We searched MEDLINE, EMBASE, PsycInfo and ERIC from Jan 2005 - June 2014. MeSH terms included “assessment,” “residency,” and “emergency medicine.” We included studies on EM residents reporting either of two primary outcomes: 1) assessment type and 2) assessment frequency per resident. Two reviewers screened abstracts, reviewed full text studies, and abstracted data. Reporting of assessment-related costs was a secondary outcome. Results: The search returned 879 articles; 137 articles were full-text reviewed; 73 met inclusion criteria. Half of the studies (54.8%) were pilot projects and one-quarter (26.0%) described fully implemented assessment tools/programs. Assessment tools (n=111) comprised 12 categories, most commonly: simulation-based assessments (28.8%), written exams (28.8%), and direct observation (26.0%). Median assessment frequency (n=39 studies) was twice per month/rotation (range: daily to once in residency). No studies thoroughly reported costs. Conclusion: EM resident assessment commonly uses simulation or direct observation, done once-per-rotation. Implemented assessment systems and assessment-associated costs are poorly reported. Moving forward, routine publication will facilitate transitioning to competency-based medical education.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.176 | 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 teacher head, 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".