INSPIRED but Tired: How Medical Faculty’s Job Demands and Resources Lead to Engagement, Work-Life Conflict, and Burnout
Bibliographic record
Abstract
BACKGROUND: Past research shows that physicians experience high ill-being (i.e., work-life conflict, stress, burnout) but also high well-being (i.e., job satisfaction, engagement). OBJECTIVE: To shed light on how medical faculty's experiences of their job demands and job resources might differentially affect their ill-being and their well-being with special attention to the role that the work-life interface plays in these processes. METHODS: Qualitative thematic analysis was used to analyze interviews from 30 medical faculty (19 women, 11 men, average tenure 13.36 years) at a top research hospital in Canada. FINDINGS: Medical faculty's experiences of work-life conflict were severe. Faculty's job demands had coalescing (i.e., interactive) effects on their stress, work-life conflict, and exhaustion. Although supportive job resources (e.g., coworker support) helped to mitigate the negative effects of job demands, stimulating job resources (e.g., challenging work) contributed to greater work-life conflict, stress, and exhaustion. Thus, for these medical faculty job resources play a dual-role for work-life conflict. Moreover, although faculty experienced high emotional exhaustion, they did not experience the other components of burnout (i.e., reduced self-efficacy, and depersonalization). Some faculty engaged in cognitive reappraisal strategies to mitigate their experiences of work-life conflict and its harmful consequences. CONCLUSION: This study suggests that the precise nature and effects of job demands and job resources may be more complex than current research suggests. Hospital leadership should work to lessen unnecessary job demands, increase supportive job resources, recognize all aspects of job performance, and, given faculty's high levels of work engagement, encourage a climate that fosters work-life balance.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".