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 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 | 0.252 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".