A meta‐analytic investigation of the personal and work‐related antecedents of work–family balance
Bibliographic record
Abstract
Summary We conducted a meta‐analysis examining antecedents of work–family balance, including personal characteristics, work demands, and work resources, as well as bidirectional conflict and enrichment. Bivariate results across 130 independent samples (N = 223 055) revealed that personal characteristics linked to more negative affect (i.e., neuroticism) and work demands (i.e., work hours, work overload, and job insecurity) were negatively associated with balance, whereas personal characteristics linked to more positive affect (i.e., extraversion and psychological capital) and work resources (i.e., job autonomy, schedule control, and workplace support) were positively related to balance. Family‐to‐work enrichment (FWE) was more strongly related to balance than was family‐to‐work conflict (FWC), and work‐to‐family conflict (WFC) was more strongly related to balance than was FWC. Finally, integrating tenets of job demands‐resources (JD‐R) theory, we examine two pathways (i.e., strain and motivation) through which antecedents relate to balance using meta‐analytic structural equations modeling (MASEM). In the strain pathway, neuroticism and job overload were negatively related to balance indirectly through higher WFC. In the motivation pathway, extraversion and job autonomy were positively related to balance indirectly through higher WFE. Work social support related positively to balance through higher WFE as well as lower WFC. We discuss theoretical and practical implications.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.023 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".