Work intensity, emotional exhaustion and life satisfaction
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine a moderated mediation model that investigated the moderating role of psychological detachment in the relationship between work intensity and life satisfaction via emotional exhaustion. Design/methodology/approach Data were collected from 149 hospital-based nurses who completed a questionnaire about working conditions and individual outcomes. The data were analyzed using hierarchical moderated regression and bootstrapping techniques. Findings The results confirm that work intensity is negatively related to life satisfaction via emotional exhaustion. The results also demonstrate that psychological detachment diminishes the negative influence of emotional exhaustion on life satisfaction. The conditional indirect effect model shows that the indirect relationship between work intensity and life satisfaction is strongest at low psychological detachment. Research limitations/implications This research advances our understanding of the negative work and non-work implications associated with work intensity. The key limitation of this research was the cross-sectional data set. HRM researchers should seek to replicate and expand the results with multi-wave data to extend our understanding of the implications of work intensity. Practical implications HRM practitioners need to begin implementing measures to address work intensity in order to thwart its negative effects. HRM practitioners need to implement policies and procedures that limit the intensity of work demands to promote positive employee work and non-work outcomes. Originality/value This is the first study to show that work intensity can influence life satisfaction through emotional exhaustion. Contrary to most recovery research, this research is also among the first to focus on the moderating role of psychological detachment, especially within a conditional indirect effect model.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".