Structure and correlates of spillover from nonwork to work: An examination of nonwork activities, well-being, and work outcomes.
Bibliographic record
Abstract
Employees today are involved in many different types of activities outside of work, including family, volunteering, leisure, and so on. The purpose of this study was to understand how participation in such nonwork activities can both enrich and interfere with well-being and behavior at work. Four dimensions of nonwork-to-work spillover were examined to better understand this process (i.e., positive emotional, negative emotional, positive behavioral, and negative behavioral). Survey data were collected in 2 waves from 293 staff and faculty members of a large Canadian university (N = 108 matched surveys from both waves). We found that volunteering is associated with increased well-being and work satisfaction, and that it creates positive emotional and behavioral, and negative behavioral spillovers. We also found that sports, recreation, and fitness are associated with improved well-being and positive emotional spillover. Negative spillover is associated with negative outcomes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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".