Using mobile technology in assessment of entrustable professional activities in undergraduate medical education
Bibliographic record
Abstract
BACKGROUND: The adoption of competency-based medical education requires objective assessments of a learner's capability to carry out clinical tasks within workplace-based learning settings. This study involved an evaluation of the use of mobile technology to record entrustable professional activity assessments in an undergraduate clerkship curriculum. APPROACH: A paper-based form was adapted to a mobile platform called eClinic Card. Students documented workplace-based assessments throughout core clerkship and preceptors confirmed accuracy via mobile phones. Assessment scores for the 2017-2018 academic year were collated and analyzed for all core rotations, and preceptors and students were surveyed regarding the mobile assessment experience. EVALUATION: The mobile system enabled 80 students and 624 preceptors to document 6850 assessment submissions across 47 clinical sites over a 48-week core clerkship curriculum. Students' scores demonstrated progressive improvement across all entrustable professional activities with stage-appropriate levels of independence reported by end of core clerkship. Preceptors and students were satisfied with ease of use and dependability of the mobile assessment platform; however, students felt quality of formative coaching feedback could be improved. REFLECTION: Our preliminary evaluation suggests the use of mobile technology to assess entrustable professional activity achievement across a core clerkship curriculum is a feasible and acceptable modality for workplace-based assessment. The use of mobile technology supported a programmatic assessment approach. However, meaningful coaching feedback, as well as faculty development and support, emerged as key factors influencing successful adoption and usage of entrustable professional activities within an undergraduate medical curriculum.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".