Redefining Emergency Medicine Procedures: Canadian Competence and Frequency Survey
Bibliographic record
Abstract
OBJECTIVE: To redefine the Royal College of Physicians and Surgeons (RCPS) procedural skills list for Canadian emergency medicine (EM) residents through a national survey of EM specialists to determine procedural performance frequency and self-assessment of competence. METHODS: The survey instrument was developed in three phases: 1) an EM program directors survey identified inappropriate or dated procedures, endorsing 127 skills; 2) a search of EM literature added 98 skills; and 3) an expert panel designed the survey instrument and finalized a list of 150 skills. The survey instrument measured the frequency of procedure performance or supervision, self-reported competence (yes/no), and endorsement of one of four training levels for each skill: undergraduate (UG), postgraduate (PG), knowledge only, or unnecessary (i.e., too infrequently performed to maintain competence). RESULTS: All 289 Canadian EM specialists were surveyed by mail; 231 (80%) responded, 221 completed surveys, and 10 were inactive. More than 60% reported competence in 125 (83%) procedures, and 55 procedures were performed at least three times a year. The mean competence score was 121 (SD +/- 17.7, median = 122) procedures. Competence score correlation with patient volume was r = 0.16 (p = 0.02) and with hours worked was r = 0.19 (p = 0.01). Competence score was not associated with year or route (residency vs grandfather) of certification. Each procedure was assigned to a training level using response consensus and decision rules (UG: 1%; PG: 82%; unnecessary: 17%). CONCLUSIONS: A survey of EM clinicians reporting competence and frequency of skill performance defined 127 procedural skills appropriate for Canadian RCPS postgraduate training and EM certification.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".