The Use of Micro-Credentials in Health Professions Education: A Scoping Review
Bibliographic record
Abstract
Background: Healthcare professionals must continuously update their competencies to keep pace with evolving clinical practices; however, traditional continuing professional development (CPD) methods often have limited impact on competency and performance. Micro-credentials have emerged as a flexible and personalized alternative to traditional CPD, yet little is known about how they are designed, implemented, and evaluated in health professions education. As educational systems invest heavily in micro-credentials, a clearer understanding of their educational value is essential. Methods: Using a scoping review guided by Arksey and O'Malley's six-stage framework and PRISMA-ScR guidelines, we systematically searched seven databases (inception-December 2024) and conducted a structured grey literature review. We examined the instructional design features, pedagogical underpinnings, assessment strategies, and reported impacts of micro-credentials. Results: We included 19 peer-reviewed papers and 35 websites describing health-related micro-credentials. Most studies were published in 2024 (42.1%), originated from the United States (42.1%), and nearly half (47.4%) provided only descriptive accounts. A wide range of instructional design features were identified, though pedagogical theories were rarely stated. Assessment strategies predominantly emphasized summative approaches (e.g., multiple-choice knowledge checks), with limited focus on higher-level competency assessment. Reported outcomes were primarily improvements in knowledge, confidence, or engagement, with no clear evidence of the distinct value of micro-credentials as a teaching modality. Discussion: Current literature offers limited evaluation of micro-credentials and often lacks theory-informed design. We infer a pedagogical foundation aligned with constructivist, context-sensitive, and stage-based principles, which may inform the development of future micro-credential programs.
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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 | 0.003 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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) | 0.000 | 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".