Seeing potential opportunities for teaching (SPOT): Evaluating a bundle of interventions to augment entrustable professional activity acquisition
Bibliographic record
Abstract
Abstract Introduction Within the Canadian competency‐based medical education system, entrustable professional activities (EPAs) are used to assess residents on performed clinical duties. This study aimed to determine whether implementing a bundle of two interventions (a case‐based discussion intervention and a rotation‐based nudging system) could increase the number of EPA assessments that could occur for our trainees. Methods The authors designed an intervention bundle with two components: 1) a case‐based workshop where trainees discussed which EPAs could be assessed with multiple cases and 2) a nudging system wherein each trainee was reminded of EPAs that would be useful to them on each rotation in their first year. We conducted a retrospective program evaluation to compare the intervention cohort (2019) to two historical cohorts using similar EPAs (2017, 2018). Results Data from 22 trainees (seven in 2017, eight in 2018, and seven in 2019) were analyzed. There was a marked increase in the total number of EPA assessments acquired in the 2019 cohort (average per resident = 285.7, 95% confidence interval [CI] = 256.1 to 312.3, range = 195–350) compared to the two other years (2018 [average = 132.4, 95% CI = 107.5 to 157.02, range = 107–167] and 2017 [70.1, 95% CI 45.3 to 91.0, range = 49–95]), yielding an effect size of Cohen's d = 4.02 for our intervention bundle. Conclusions Within the limitations of a small sample size, there was a strong effect of introducing two interventions (a case‐based orientation and a nudging system) upon EPA assessments with PGY‐1 residents. These strategies may be useful to others seeking to improve EPA assessment numbers in other specialties and clinical environments.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".