A combination of work environment factors and individual difference variables in work interfering with family
Bibliographic record
Abstract
Purpose The purpose of this paper is to test the relative contribution of work environment factors as well as individual difference variables on the degree of work interfering with family (WIF) and other mental health outcomes, namely, emotional exhaustion, life satisfaction, and family interfering with work (FIW). Design/methodology/approach Self‐report measures of the constructs of interest will be completed by a random sample of 539 health care professionals (Study 1: n=314; Study 2: n=128). In Study 1, it is hypothesized that work environment factors namely, work stressors and a supportive work environment characterized by perceived support from the supervisor, the organization, and co‐workers' supportive behaviors will be positively and negatively associated with WIF, respectively. Findings Findings document positive links between task‐related stressors and WIF and negative links between perceived support from the organization and WIF. In addition, both task‐related stressors and WIF are positive predictors of emotional exhaustion. In Study 2, the relative impact of two individual difference variables (i.e. time management and global self‐determination) on WIF and other mental health outcomes are examined, above and beyond the impact of the work environment factors. Task‐related stressors remainean important predictor of WIF and global self‐determination accounts for additional variance in this outcome variable. Research limitations/implications Theoretical and practical implications that may guide future theory and research in this domain are discussed. Originality/value Findings from both studies provide insight as to potential sources, namely work environment factors and individual difference variables, which may accentuate or mitigate the degree of WIF.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".