Peer‐assisted bedside teaching rounds
Bibliographic record
Abstract
BACKGROUND: Although postgraduate trainees play a well-accepted role in medical education, little consideration has traditionally been given to senior undergraduate trainees as teachers. Recently, research has shown senior medical students (SMS) can play an effective teaching role for junior medical students (JMS) in non-clinical medical settings. PURPOSE: The purpose of our study was to understand the perceptions of SMSs as teachers in a clinical environment for JMS. METHOD: All students who participated in our peer-led bedside teaching programme from September 2010 to May 2012 were invited to complete a questionnaire following their teaching session. Fifty-six of 70 JMS (80%) and 15 of 15 SMS (100%) participated. Survey questions addressed learning, bedside experiences, teacher effectiveness and the overall usefulness of these sessions. The data collected were analysed for significance of the perceptions reported. RESULTS: We found students reported positive and statistically significant results in all domains examined. JMS reported that sessions were highly valuable learning, improved confidence and comfort at the bedside, had excellent teaching and were a valuable addition to their clinical skills training. SMS reported getting highly valuable learning through preparation and developing improved comfort in a teaching role. Little consideration has traditionally been given to senior undergraduate trainees as teachers CONCLUSIONS: Our findings demonstrate that peer-directed learning in undergraduate medical education can be effectively implemented in the clinical arena.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".