Work-Family Enrichment Training: Promoting Transferability of Resources across Life Domains
Bibliographic record
Abstract
The goal of this work was to examine intentional resource transfer across work and family roles. This was accomplished by developing and evaluating a work-to-family enrichment training program based on Greenhaus and Powell’s (2006) enrichment theory, interpersonal capitalization theory (e.g., Ilies, Keeney, & Scott, 2011) and social cognitive theory (Bandura, 1986). The program was administered to 163 administrative employees at a large North American university using a pre-post experimental design. Consistent with hypotheses, findings indicated that, compared to a control training, the enrichment training increased development-based enrichment from work to family (the beneficial transfer of skills, knowledge and values). The effect of the training on both development-based and affect-based enrichment (the transfer of positive affect) was mediated by enrichment self-efficacy. Further, the indirect effects of the training on enrichment were moderated by initial social-emotional work resources, such that the training was more effective for individuals initially perceiving a relatively low level of such resources. Finally, as hypothesized, enrichment self-efficacy and enrichment experiences sequentially mediated the effect of the training on job satisfaction. This work advances theory by demonstrating that human agency can facilitate resource transfer across roles. It advances practice by devising and testing a work-family enrichment training program that can serve as a tool for organizations to increase positive synergy between work and non-work and enhance employee well-being.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".