Resident experiences of informal education: how often, from whom, about what and how
Bibliographic record
Abstract
CONTEXT: The merits of informal learning have been widely reported and embraced by medical educators. However, research has yet to describe in detail the extent to which informal intraprofessional or informal interprofessional education is part of graduate medical education (GME), and the nature of those informal education experiences. This study seeks to describe: (i) who delivers informal education to residents; (ii) how often they do so; (iii) the content they share; and (iv) the teaching techniques they use. METHODS: This study describes instances of informal learning in GME captured through non-participant observations in two contexts: a palliative care hospice and a paediatric hospital. Analysis of 60 hours of observation data involved a process of collaborative team consensus to: (i) identify instances of informal intraprofessional and informal interprofessional education, and (ii) categorise these instances by CanMEDS Role and teaching technique. RESULTS: Findings indicate that 84.8% of GME-level informal education that takes place in these two settings is physician-led and 15.2% is nurse-led. Organised by CanMEDS Role, findings reveal that, although all Roles are addressed by both physicians and nurses, those most commonly addressed are Medical Expert (physicians: 35.7%; nurses: 27.5%) and Communicator (physicians: 22.3%; nurses: 25.0%). Organised by teaching technique, findings reveal that physicians and nurses favour similar techniques. CONCLUSIONS: Although it is not surprising that informal interprofessional education plays a lesser role than informal intraprofessional education in GME, these findings suggest that the role of informal interprofessional education is worthy of support. Echoing the calls of others, we posit that medical education should recognise and capitalise on the contributions of informal learning, whether it occurs intra- or interprofessionally.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".