Identifying Expert-Informed Social Media Entrustable Professional Activities (EPAs) for Health Professions Learners
Bibliographic record
Abstract
Healthcare professionals extensively use social media. Despite initiatives to teach about its use, there remains a gap in effective assessment methods for determining when learners are ready for professional social media engagement. The objective of this study is to develop Entrustable Professional Activities (EPAs) to support competency-based assessment of learners’ social media use. From June to October 2022, the primary author team (IZ, STL, TK, DH, MM) engaged 37 experts in health professions-related social media use from multiple specialties in a multi-round, online, modified Delphi process to develop EPAs for healthcare professionals’ social media use. These physicians are recognized in their fields as social media leaders, having contributed to publications on social media, assumed leadership roles in social media in journals or institutions, developed social media use curricula, and/or were prolific social media users. They evaluated EPA statements drafted by the primary authors on a 5-point Likert-like scale and observability (yes/no). EPAs rated as extremely/very/moderately important and observable by at least 70% of participants were selected for the final EPA statement set. Descriptive statistics facilitated quantitative analysis. Out of 32 participants who accepted the invite, 24 (75%) completed all three rounds of the study. Following the third round, a list of 8 EPAs was finalized, with > 80% consensus on all EPAs. These 8 social media EPAs were categorized into the Professional Development, Education, and Advocacy domains. Expert-informed EPAs for health professionals’ social media use may guide those in training using social media in the domains of Professional Development, Education, and Advocacy.
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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 | 0.019 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".