Students’ perception of the characteristics of effective bedside teachers
Bibliographic record
Abstract
BACKGROUND AND METHODS: To determine a student perspective of the characteristics of ideal bedside teachers, a 25-item questionnaire was administered to 84 final-year medical students. The items were constructed to check for two domains of 'Communication' and of 'Demographics'. The former included behaviours such as providing constructive feedback, respecting patient confidentiality and encouraging critical thinking, while the latter included characteristics such as gender, academic rank and language skills. RESULTS: The students identified the characteristics in the 'Communication' domain as being far more important determinants of ideal bedside teaching than the 'Demographics' domain. Factor analysis showed that of the questions designed to determine communication all but one loaded unequivocally into a single factor, while the demographics were best described by two additional factors. Both these factors represented teacher properties that were difficult or impossible for the teacher to modify, while those in the communication domain were all amenable to change. CONCLUSIONS: These results are consistent with data from the literature on the broader aspects of clinical teaching, and imply that the ideal bedside teaching experience from the perspective of the students is heavily influenced by teacher behaviours than that can be modified.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".