Bedside teaching: an underutilized tool in medical education
Bibliographic record
Abstract
Bedside teaching (BST) is a fundamental component of clinical training and an essential tool in the creation of a competent physician.1-15 Sir William Osler (1849-1919), one of Canada’s most renowned physicians, was the first to introduce BST to medical education in 1892. He described modern medical education as something that needed to be taught at the bedside: “Medicine is learned by the bedside and not in the classroom.”9 BST allows the physician and patient to interact at the bedside; through this physician-patient interaction process, medical students and residents are simultaneously afforded the opportunity to learn clinical skills, clinical reasoning, physician-patient communication, empathy, and professionalism.6,12,15 In real practice, comprehensive history taking can help the physician diagnose up to 56% of patient problems, which may rise to 73% if a physical examination is added.8 Much information can be gained and a proper diagnosis reached by obtaining a good medical history and performing an efficient clinical examination.8 Clinical teaching in which the patient is involved is enriched by these visual, auditory, and tactile experiences. Senior medical students and medical residents believe that BST is a valuable but underutilized tool.15 Time spent on BST has been on the decline since 1978, as highlighted by Ahmed, who reported that the proportion of teaching time taken up by BST had declined from 75% 30 years ago to only 16% today.8 The learning triad The BST learning triad comprises patients, students, and tutors.6 All three must be present for BST to occur and it must occur within a clinical environment. Each individual member brings his or her own value to the learning triad. For example, the student brings medical knowledge and the eagerness to learn; the tutor brings depth of knowledge, mentorship, and willingness to help the student learn and make connections; and finally, the patient brings relevant clinical issues to the forefront that allow the student to learn. An effective learning environment requires all three groups to work together in the learning triad.6 The obstacles that may reduce the effectiveness of BST can be categorized by each group in the learning triad.
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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 | 0.012 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".