Bibliographic record
Abstract
Purpose To increase understanding of informal learning in practice (e.g., consulting with colleagues, reading journals) through exploring the experiences and perceptions of physicians perceived to be performing well. Objectives were to find out how physicians learned in practice and maintained their competence, and how they learned about the communication skills domain specifically. Method Of 142 family physicians participating in a formal multisource feedback (360-degree) formative assessment, 25 receiving high scores were invited to participate in interviews conducted in 2003 at Dalhousie University Faculty of Medicine. Twelve responded. Interviews were 1.5 hours each, recorded, transcribed, and analyzed by the research team using accepted qualitative procedures. Results While formal learning appeared important to most, informal learning, especially through patients and colleagues, appeared to be fundamental. The physicians appeared to learn intentionally from practice and work experiences, and reflection appeared integral to learning and monitoring the impact of learning. Two findings were surprising: participants’ conceptions of competence and perceptions that communication skills were innate rather than learned. Conclusions These physicians’ ways of intentional learning from practice concur with current models of informal learning. However, informal learning is largely unrecognized by formal institutions. Additionally, the physicians did not in general share notions of professional competence held by educators and others in authority. These findings suggest the need to make implicit content and learning processes more explicit. Additional research areas include exploring whether physicians across the range of performance levels demonstrate similar processes of reflective 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 | 0.001 | 0.004 |
| 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 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.021 |
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".