Work-Life Balance: The Good and the Bad of Boundary Management
Bibliographic record
Abstract
Work-life balance is an important issue in today’s world and the different strategies used by people to manage their work and their personal life can have a great impact. Two studies were conducted (study 1: n = 117; study 2: n = 293) to examine how boundary segmentation preferences (studies 1 & 2) and boundary integration strategies (study 2) affect work-family conflict and enrichment. Results from structural equation modeling partly confirmed the hypothetical model in both studies. Study 1 showed that work-home segmentation preference negatively predicted work-family enrichment, while home-work segmentation preference negatively predicted family-work enrichment. Study 2 provided similar results, as it showed that work-home segmentation preference negatively predicted work-family enrichment. It also showed that work-home segmentation preference positively predicted work-family conflict and home-work segmentation preference positively predicted work-family enrichment, while work-life integration strategy positively predicted work-family conflict, family-work conflict, work-family enrichment and family-work enrichment. No significant relationship was found between life-work integration strategy and any of the dependent variables. Findings from these studies highlight the importance of using appropriate boundary management strategies in order to promote a better work-life balance. They also enhance current knowledge related to boundary management and work-life balance by examining relationships with work-family enrichment.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".