Understanding the relationship between prior to end-of-workday physical activity and work–life balance: A within-person approach.
Bibliographic record
Abstract
Although physical activity has typically been conceptualized by organizational scholars as a postwork activity that spills over to enhance work-related experiences, little is known about how physical activity prior to the end of the workday spills over to affect nonwork criteria. Drawing from Hirschi, Shockley, and Zacher's (2019) action regulation model of work-life balance, we develop a process-oriented model of the implications of prior to end-of-workday physical activity for daily satisfaction with work-life balance. We examine our conceptual model in a 5-day daily diary study that incorporates objective measurements of physical activity (i.e., prior to end-of-workday steps assessed via actigraph) collected from 71 full-time employees. Consistent with our predictions, prior to end-of-workday physical activity yields greater levels of end-of-workday vigor, a boundary-spanning resource that in turn provides the energetic bandwidth to simultaneously achieve work-related (i.e., daily work recovery) and non-work-related (i.e., daily family absorption) goals during the postwork period, ultimately enhancing daily satisfaction with work-life balance. We discuss how our findings expand the scope of theorizing surrounding employee physical activity to encompass nonwork criteria and yield actionable recommendations to harness prior to end-of-workday physical activity as a positive resource. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".