Original Article Study of the Factors Influencing the Stimulus to Learning Recorded by Physicians Keeping a Learning Portfolio
Bibliographic record
Abstract
Abstract:While studies in continuing education have identified the information sources most frequently used by physicians for learning, little is known of what stimulates physicians to engage in learning activities that lead to a commitment to adopt a new practice. This study reports on the recorded stimulus for learning of 8576 items of learning submitted by 652 physicians who voluntarily enrolled in the Maintenance of Competence program (MOCOMP®) of the Royal College of Physicians and Surgeons of Canada and used a paper or electronic diary (PCDiary®) to record self-directed learning activities. The most frequent stimuli for initiating learning were reading the medical literature and managing patients. The only demographic variable that significantly influenced the item stimulus profile of these physicians was the number of years since graduation (p =.0001). Physicians less than 10 years from graduation more frequently recorded learning items stimulated by an audit of practice and less frequently by a discussion with peers compared with physicians in practice more than 10 years. Physicians in practice for more than 30 years initiated learning activities primarily based on their interaction with patients. There was no significant relationship between the item stimulus profile and the physicians’specialty type (p =.47), size of the community where their practice is
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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 | 0.002 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".