Procedural Skills Training in Emergency Medicine Physicians Within the Edmonton Zone: A Needs Assessment
Bibliographic record
Abstract
OBJECTIVES: The objectives were to describe the current procedural skill practices, attitudes toward procedural skill competency, and the role for educational skills training sessions among emergency medicine (EM) physicians within a geographic health zone. METHODS: This is a multicenter descriptive cross-sectional survey of all EM physicians working at 12 emergency departments (EDs) within the Edmonton Zone in 2019. Survey items addressed current procedural skill performance frequency; perceived importance and confidence; current methods to maintain competence; barriers and facilitating factors to participation in a curriculum; preferred teaching methods; and desired frequency of practice for each procedural skill. RESULTS: Survey response rate was 53.6%. Variability in frequency of performed procedures was seen across the type of hospital sites. For the majority of skills, there was a significantly positive correlation between the frequency at which a skill was performed and the perceived confidence performing said skill. There was inconsistency and no significant correlation with perceived importance, perceived confidence or frequency performing a given skill, and the desired frequency of training for that skill. Course availability (76.2%) and time (72.8%) are the most common identified barriers to participation in procedural skills training. CONCLUSIONS: This study summarized the current ED procedural skill practices among EM physicians in the Edmonton Zone and attitudes toward an educational curriculum for procedural skill competency. This represents a step toward targeted continuing professional development in staff physicians.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".