A longitudinal examination of the work–nonwork boundary strength construct
Bibliographic record
Abstract
Abstract Many organizations are blurring the boundaries between work and nonwork through practices such as flextime, telecommuting, and on‐site day‐cares. Such integration of work and nonwork is purported to help employees find the seemingly elusive “work‐life balance.” Scholarly investigations of this issue have increased in number, but a standard measure of work–nonwork boundary strength has yet to emerge. The purpose of this research is to explore the boundary strength construct through the process of measure validation. In Study 1, data were collected from students ( N = 162) to pilot test the measure. Study 2 was a longitudinal field study in which data were collected from employees of Canadian organizations (Survey 1: N = 793; Matched data for Surveys 1 & 2: N = 205). Confirmatory factor analyses supported the hypothesized two‐factor structure of the work–nonwork boundary strength measure, confirming the importance of differentiating boundary strength at home (BSH) and boundary strength at work (BSW). Longitudinal analyses confirmed the structural invariance of the measure and revealed that boundary strengths are relatively stable over a period of 1 year. Role identification was related to boundary strength at home only. Weak boundaries, both at home and at work, were associated with high inter‐role conflict. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".