Learning Through Teaching: How Physicians Learn Medicine in Authentic Clinical Contexts
Bibliographic record
Abstract
PURPOSE: Little is known about the clinical knowledge and skills that are acquired by physicians through teaching, how such learning occurs, or the factors that influence this process. This study explored how physicians acquire clinical knowledge and skills through clinical teaching and examined the contextual elements that influence this learning. METHOD: Two theoretical frameworks informed this interpretive description study: situated learning and cognitive apprenticeship. From March to November 2021, semistructured interviews and follow-up discussions were conducted at McGill University with clinician-teachers who regularly supervise internal medicine residents. Participants were asked to describe how they learned clinical medicine through spontaneous clinical teaching, guided by questions relating to what they learned, memorable teaching moments, and factors influencing this learning. Data were analyzed iteratively, using both a deductive and inductive approach. RESULTS: Of the 87 contacted physicians, 45 responded, expressing interest (n = 22) or declining participation (n = 23), and 42 did not respond. All 22 clinicians who responded positively were interviewed, with 7 follow-up discussions. Results suggested that clinician-teachers encountered myriad opportunities to learn clinical medicine during spontaneous interactions with trainees. These interactions, embedded in authentic patient care, were influenced by clinician-teacher characteristics, trainee characteristics, and contextual affordances. Clinician-teachers were stimulated to learn by trainee presence and through discrete interactions with trainees. These stimuli often led to feelings of "performative pressure" to role model and teach effectively or "slowing down" in thinking, prompting clinician-teachers to engage in learning processes (e.g., reflection, collaboration, and articulation), which resulted in knowledge acquisition, reinforcement, and refinement. CONCLUSIONS: Learning through teaching is an underappreciated strategy that can help clinician-teachers improve their clinical knowledge and skills. This study uncovered some of the processes through which clinicians learn during spontaneous clinical teaching and the factors that modulate this learning.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".