Can a multisensory teaching approach impart the necessary knowledge, skills, and confidence in final year medical students to manage epistaxis?
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study is to evaluate the efficacy of a multisensory teaching approach in imparting the knowledge, skills, and confidence to manage epistaxis in a cohort of fourth year medical students. METHODS: One hundred and thirty four fourth year medical students were recruited into the study from Aug 2011 to February 2012 in four groups. Students listened to an audio presentation (PODcast) about epistaxis and viewed a video presentation on the technical skills (VODcast). Following this, students completed a 5-minute Individual Readiness Assessment Test (IRAT) to test knowledge accrued from the PODcast and VODcast. Next, students observed a 10-minute expert demonstration of the technical skills on a human cadaver and spent half an hour practicing these techniques on cadaver simulators with expert guidance. The students' confidence was assessed with Confidence Level Questionnaires (CLQs) before and after their laboratory session. The skill level of a subset of students was also assessed with a pre- and post-laboratory Objective Structured Assessment of Technical Skills (OSATS). RESULTS: Eighty two percent of the participants achieved a score of at least 80% on the IRAT. The CLQ instrument was validated in the study. There was a statistically significant improvement between the pre- and post-laboratory CLQ scores (p<0.01) and also between pre- and post-laboratory OSATS scores (p<0.01). Qualitative feedback suggested a student preference for this teaching approach. CONCLUSIONS: This study provides further evidence that a multisensory teaching intervention effectively imparts the necessary knowledge, skill and confidence in fourth year medical students to manage epistaxis.
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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 | 0.001 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".