Disentangling the relationship between gender and work–family conflict: An integration of theoretical perspectives using meta-analytic methods.
Bibliographic record
Abstract
Implicit in many discussions of work-family issues is the idea that managing the work-family interface is more challenging for women than men. We address whether this intuition is supported by the empirical data via a meta-analysis of gender differences in work-family conflict (WFC) based on more than 350 independent samples (N > 250,000 workers). Challenging lay perceptions, our results demonstrate that men and women generally do not differ on their reports of WFC, though there were some modest moderating effects of dual-earner status, parental status, type of WFC (i.e., time-, strain-, vs. behavior-based), and when limiting samples to men and women who held the same job. To better understand the relationship between gender and WFC, we engaged in theory-testing of mediating mechanisms based on commonly invoked theoretical perspectives. We found evidence in support of the rational view, no support for the sensitization and male segmentation perspectives, and partial support for the asymmetrical domain permeability model. Finally, we build theory by seeking to identify omitted mediators that explain the relationship between gender and work-interference-with-family, given evidence that existing theoretically specified mechanisms are insufficient to explain this relationship. Overall, we find more evidence for similarity rather than difference in the degree of WFC experienced by men and women. (PsycINFO Database Record
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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 | 0.097 | 0.198 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".