Lifelong learning practices and leisure-time exercise habits of academic and community-based physicians
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. Objective: Physicians are required to be lifelong learners for the provision of quality patient care. At the same time, physician wellbeing is a critical component in the delivery of such care. This study was designed to examine: (1) lifelong learning practices and leisure-time exercise habits of academic and community-based physicians; and (2) associations of leisure-time exercise with work engagement, exhaustion, and professional life satisfaction. Methods: Using an online survey, quantitative data were collected from physicians practicing in Canada. The survey contained validated scales of physician lifelong learning, leisure-time exercise, work engagement, work exhaustion, and professional life satisfaction. Descriptive, chi-square, t-test, and correlational analyses were performed. Results: Physicians (n=57) reported moderately high levels of lifelong learning, with no significant difference between academic and community-based physicians. To stay current in their practice, the majority of physicians reported exchanging ideas/asking colleagues and searching databases as questions arise (>90%), followed by engaging in clinical teaching and attending conferences and meetings of professional organizations (>80%). Watching podcasts and webinars was the least preferred lifelong learning activity (<50%). With respect to leisure-time exercise habits, more community-based physicians reported engaging in mild and/or moderate forms of exercising, whereas more academic physicians reported engaging in strenuous exercising in a typical week. Correlational analyses revealed that physicians' leisure-time exercise scores were positively correlated with professional life satisfaction (r = 0.25; p = 0.058) and work engagement (r = 0.29; p = 0.028) and negatively correlated with work exhaustion (r = −0.34; p = 0.01). Conclusions: Irrespective of the practice type, physicians tend to engage in lifelong learning activities that offer in-person interactions with colleagues and trainees. Regular participation in leisure-time exercise appears to enhance physicians' professional wellbeing. As such, these activities and habits should be encouraged, supported, and promoted within institutional culture and health systems in general.
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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 | 0.000 | 0.004 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".