Does self-regulation capacity predict psychological well-being in physicians?
Bibliographic record
Abstract
Despite increasing research on physician well-being, factors appearing to account for individual variation in levels of optimal functioning are largely unclear. One such factor could be self-regulation, which reflects how individuals effectively manage their thoughts, emotions and behaviours, and cope with adversity in their environment. The purpose of this study was to determine if self-regulation capacity could significantly predict psychological well-being in a sample of Canadian physicians. A total of 132 physicians completed the Scales of Psychological Well-Being and the short form of the Self-Regulation Questionnaire. Regression analyses confirmed the hypothesis that a significant amount of variance in levels of psychological well-being would be explained by self-regulation capacity. There was a particularly strong relationship between self-regulation capacity and the dimensions of purpose in life and environmental mastery, which suggests that physicians who effectively self-manage may be better able to preserve a sense of purpose and an adequate work-life balance in their daily life. Physicians today face consistently growing demands stemming from increasingly challenging work environments. Results of this study mark an important step in increasing our understanding of a potentially valuable skill that may help physicians to achieve well-being.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".