Experiential Faculty Development to Increase the Number of Entrustable Professional Activity Assessments
Bibliographic record
Abstract
BACKGROUND: Emergency medicine (EM) residents must complete both adult and paediatric entrustable professional activities (EPAs). During their paediatric emergency medicine rotation at a university paediatric hospital, EM residents struggled to receive EPA assessments because preceptors had not yet been trained due to the stepwise implementation of EPAs. This study aimed to evaluate the impact of a workshop on behaviour change by measuring the number of EPA assessments. METHODS: A comparative pretraining and posttraining study involving 27 invited faculty members was conducted to assess the impact of a faculty development programme. The training was delivered via videoconference with experiential learning techniques to practise every aspect of the supervision of an EPA, including selecting the appropriate EPA according to mirroring real-world situations, giving feedback, evaluating autonomy and recording the EPA in the resident's logbook. RESULTS/FINDINGS: In total, 20 out of 27 eligible faculty members (74%) agreed to participate in the study. Their main challenges reported were a lack of trainee initiative, preceptor training and competence in supervising EPAs. Over the 12-month analysis period, the enrolled faculty assessed 125 EPAs for 38 EM residents, including 52 pre-intervention EPAs and 73 post-intervention EPAs. Calculation of data points above the median showed a 1-point difference in the EPAs assessments to resident ratio between the pre- and post-intervention periods (3/7 vs. 4/7). CONCLUSION: Our findings suggest that faculty training using multiple educational strategies may enable EM residents to receive more EPA assessments during their paediatric emergency medicine rotation.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".