A meta‐analysis of the antecedents of work–family enrichment
Bibliographic record
Abstract
Summary This study meta‐analytically examined theoretically derived antecedents of both directions of work–family enrichment (sometimes labeled facilitation or positive spillover), namely, work–family enrichment and family–work enrichment. Contextual and personal characteristics specific to each domain were examined. Resource‐providing (e.g., social support and work autonomy) and resource‐depleting (e.g., role overload) contextual characteristics were considered. Domain‐specific personal characteristics included the individuals' psychological involvement in each domain, the centrality of each domain, and work engagement. Results based on 767 correlations from 171 independent studies published between 1990 and 2016 indicate that several contextual and personal characteristics have significant relationships with enrichment. Although those associated with work tend to have stronger relationships with work–family enrichment and those associated with family tend to have stronger relationships with family–work enrichment, several antecedent variables have significant relationships with both directions of enrichment. Resource‐providing contextual characteristics tend to have stronger relationships with enrichment than do resource‐depleting characteristics. There was very little evidence of gender being a moderator of relationships between contextual characteristics and enrichment. Lastly, meta‐analytic structural equation modeling provided evidence that a theoretical path model wherein work engagement mediates between several contextual characteristics and enrichment is largely generalizable across populations.
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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 | 0.027 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| 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".