Bibliographic record
Abstract
This paper examines how different coping styles that physicians use relate to emotional exhaustion, the key defining dimension of burnout. Specifically, we examine the extent to which they use active problem solving techniques, seek support, disengage from the situation or use denial as a coping strategy. In addition, we also explore whether the coping styles are more or less effective depending on certain dispositional and/or situational factors. Two individual predispositions are examined in this study in terms of positive and negative affectivity, as optimism and pessimism are stable personality traits that have implications for how individuals view situations and respond to them. Four different sources of physician work stress are examined to reflect the situational factors: work overload, patient interactions, average weekly work hours at work, and average weekly work hours at home. We analyze survey data from 1,110 practising physicians in a single health region in Western Canada. The overall pattern of results suggests that physicians’ individual dispositions are relevant to understanding the coping styles that they adopt. Physicians appear to use denial as a coping strategy when they experience work overload and difficult patient interactions. Furthermore, it is used by those with high negative affectivity. However, having a highly positive outlook appears to neutralize the harmful relationship between denial and emotional exhaustion. This supports the literature that argues that the effects of different coping styles may depend on the personality traits of who uses them. In addition, the harmful experiences related to stressful patient interactions are weakened for doctors who disengage or take a time out from the situation. This supports the literature that suggests that certain coping strategies may be more effective depending on the situation or type/source of stressor. Our findings suggest that certain coping strategies may be more effective depending on personality type and the type or source of stress encountered.
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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 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".