Performance criteria for emergency medicine residents: a job analysis.
Bibliographic record
Abstract
OBJECTIVE: A major role of admission interviews is to assess a candidate's suitability for a residency program. Structured interviews have greater reliability and validity than do unstructured ones. The development of content for a structured interview is typically based on the dimensions of performance that are perceived as important to succeed in a particular line of work. A formal job analysis is normally conducted to determine these dimensions. The dimensions essential to succeed as an emergency medicine (EM) resident have not yet been studied. We aimed to analyze the work of EM residents to determine these essential dimensions. METHODS: The "critical incident technique" was used to generate scenarios of poor and excellent resident performance. Two reviewers independently read each scenario and labelled the performance dimensions that were reflected in each. All labels assigned to a particular scenario were pooled and reviewed again until a consensus was reached. RESULTS: Five faculty members (25% of our total faculty) comprised the subject experts. Fifty-one incidents were generated and 50 different labels were applied. Eleven dimensions of performance applied to at least 5 incidents. "Professionalism" was the most valued performance dimension, represented in 56% of the incidents, followed by "self-confidence" (22%), "experience" (20%) and "knowledge" (20%). CONCLUSION: "Professionalism," "self-confidence," "experience" and "knowledge" were identified as the performance dimensions essential to succeed as an EM resident based on our formal job analysis using the critical incident technique. Performing a formal job analysis may assist training program directors with developing admission interviews.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".