Can you teach a hands-on skill online? A scoping review of e-learning for point-of-care ultrasound in medical education
Bibliographic record
Abstract
Background: There is an increasing need and interest in teaching point-of-care ultrasound (POCUS) online. The objective of this study was to systematically review the literature regarding e-learning as a method for teaching POCUS in medical education, to assess the benefits and limitations of various styles of e-learning for POCUS, and to identify gaps in the literature that could help guide future research in this field. Methods: A literature search was conducted on three databases including MEDLINE (Ovid), EMBASE and Cochrane Central Register of Controlled Clinical Trials on October 12, 2021, retrieving a total of 1630 studies. 31 studies met the inclusion and exclusion criteria. These studies were separated into different styles of e-learning and learner outcomes were analyzed based on Kirkpatrick's hierarchy. Results: The studies were categorized into three styles of e-learning: a) blended learning b) online-only (asynchronous or synchronous) and c) use of handheld machines or telesonography. POCUS knowledge and image interpretation were successfully taught online, however online-only learning for image acquisition was not as consistently effective. Blended learning and telesonography were beneficial for learning image acquisition skills. Generally, novice learners benefited most from e-learning. Conclusion: E-learning for POCUS is gaining in popularity in recent years. POCUS is a complex technical skill, and depending on the individual task being taught, different styles of e-learning may be more successful. These findings can inform future POCUS educational programs.
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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 | 0.015 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".