Work–nonwork conflict and burnout: A meta-analysis
Bibliographic record
Abstract
This study meta-analytically examines correlations between dimensions of work–nonwork conflict (work-to-nonwork and nonwork-to-work conflict) and burnout subscales (exhaustion, depersonalization/cynicism), with a special emphasis on the role of moderating variables. The meta-analysis is based on 220 coefficients from 91 samples with a total of 51,700 participants and employs a random-effects model. Primary studies relied on samples of working adults from different cultural backgrounds. Our results revealed that both directions of work–nonwork conflict were strongly related to emotional exhaustion as well as to cynicism (ρ between .34 and .61). The correlations were shown to be moderated differentially by gender, age, marital and parental status as well as by cultural background. Meta-analyses based on primary studies with multi-wave designs indicated that work interfering with nonwork and exhaustion have equal reciprocal effects when considering zero-order correlations. However, within meta-analytical structural equation modeling, cross-lagged relations between work-to-nonwork conflict and exhaustion across time did not improve the prediction of outcomes at Time 2 above the influence of stability coefficients.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.030 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".