Work-Life Balance: The Good and the Bad of Boundary Management
Bibliographic record
Abstract
<p>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 &amp; 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.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".