Physicians as Teachers and Lifelong Learners
Bibliographic record
Abstract
INTRODUCTION: Lifelong learning requires sustained motivation for learning. Employing a motivational theory framework, we investigated the relationships of psychological need satisfaction, clinical teaching involvement, and lifelong learning of physicians at different career stages and in various medical specialties. We also examined the associations of physician lifelong learning with stress, burnout, teaching enjoyment, and life satisfaction, all of which are essential for physician well-being and, ultimately, for the provision of quality patient care. METHODS: This was a cross-sectional study. Using survey methodology, quantitative data were collected from 202 practicing physicians in Canada. The questionnaire contained validated scales of physician lifelong learning and psychological need satisfaction, measures of clinical teaching (involvement and enjoyment), stress level, burnout frequency, and life satisfaction. Analysis of covariance and correlational analysis were performed. RESULTS: On average, participants reported moderate to moderately high levels of lifelong learning, psychological need satisfaction, teaching enjoyment, and life satisfaction. Irrespective of career stage and specialty, physicians' psychological need satisfaction and involvement in clinical teaching were significant in relation to lifelong learning. That is, physicians who experienced greater psychological need satisfaction at work and those who were involved in clinical teaching had, on average, higher lifelong learning scores. Physician lifelong learning had significant associations with life satisfaction and teaching enjoyment but not with stress level and burnout frequency. DISCUSSION: Fulfilling physicians' basic psychological needs at work and supporting them in their teaching roles is likely to enhance physician lifelong learning and, ultimately, quality of patient care.
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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 | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".