Frontline learning of medical teaching: “you pick up as you go through work and practice”
Bibliographic record
Abstract
BACKGROUND: Few medical teachers have received formal teaching education. Along with individual and organizational barriers to participation in teacher training programs, increasing numbers and altered distribution of physicians away from major teaching centers have increased the difficulty of attendance. Furthermore, it is not known if traditional faculty development formats are the optimal learning options given findings from existing studies document both positive and negative outcomes. There is a gap in research that explores how medical teachers learn to teach and also limited research regarding how medical teachers actually teach. The purpose of this study was to provide insight into how physicians describe their teaching of trainees, and the nature of their teaching development and improvement to inform faculty development programs. METHODS: Semi-structured interviews were conducted with 36 physicians, with a broad range of teaching experience, purposefully selected from five disciplines: Internal Medicine, Pediatrics, Psychiatry, Surgery, and Family Medicine. A qualitative, inductive approach was used to analyse the data. RESULTS: Teaching was described as being centered on the needs of individual trainees, but was dependent on patient presentation and environmental context. For this group of physicians learning to teach was perceived as a dynamic and evolving process influenced by multiple life experiences. The physicians had not learnt to teach through formal education and then put that learning into practice, but had learnt to teach and improve their teaching through their trial and errors teaching. Life experiences unconnected with the medical environment contributed to their knowledge of teaching along with limited formal learning to teach experiences. Teaching practice was influenced by peers and trainees, feedback, and observation. The findings suggest these medical teachers learn to teach along a continuum largely through their teaching practice. CONCLUSION: The findings suggested that the participants' major resource for learning how to teach was informal experiential learning, both in and out of the workplace. This may have implications for faculty development strategies for medical teaching education.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".