Research: Emergency medicine residents’ acquisition of point-of-care ultrasound knowledge and their satisfaction with the flipped classroom andragogy
Bibliographic record
Abstract
Background: One of the traditional approaches for knowledge transfer in medical education is through face-to-face (F2F) teaching. We aimed to evaluate the acquisition of knowledge about point-of-care ultrasound (POCUS) and learner’s satisfaction with the flipped classroom (FC) teaching approach. Methods: This was a prospective, mixed-method, crossover study and included 29 emergency medicine (EM) residents in current training program. Over a period of three months, each resident was exposed to F2F and FC teaching models in a crossover manner. There was a multiple-choice questions (MCQ) test before and after each educational intervention (F2F & FC). Two months after each educational intervention a final MCQ test was administered to assess the retention of knowledge between the two approaches. After each educational approach feedback was sought from a selected group of residents concerning the acceptability of the two educational approaches through a semi structured interview. Results: A total of 29 EM residents participated in this study. The numbers of residents by year of post-graduation training were seven (24.14%) PGY-1, eight (27.59%) PGY-2, six (20.69%) PGY-3, and eight (27.59%) PGY-4. The baseline mean score was 15.82 using MCQs test mean scores. For the face-to-face teaching model, the difference between pre and post-intervention scores was 2.7 (95% CI 2.1 to 3.3, p=0.001); whereas, for the flipped classroom teaching model, the difference was 3.93 (95% CI 3.2 to 4.5, p= 0.001). At two months post-intervention, for face-to-face teaching model, the MCQ assessment showed an increase of 1.7 (95% CI 1.1 to 2.2, p= 0.001) mean scores when compared to the pre-intervention mean scores; whereas, for the flipped classroom model this difference was significantly higher, recorded as 4.48 (95% CI 3.7 to 5.1, p= 0.001). Finally, the difference between mean scores for F2F and FC teaching models was 2.75 (95% CI 1.87 to 3.64, p=0.001) at two months post-intervention. Overall, the participants expressed a preference for the FC teaching methodology. Conclusion: Both F2F and FC teaching methods resulted in significant and sustained improvements in POCUS knowledge base. The FC teaching method accomplished higher test scores than the F2F teaching method both at the end of the teaching and after two months of completing the educational program.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".