Commuting stress process and self‐regulation at work: Moderating roles of daily task significance, family interference with work, and commuting means efficacy
Bibliographic record
Abstract
Abstract Based on self‐regulation theories of stress processes, this study proposed a model to examine the within‐person mediation relationship between morning commuting stressors and self‐regulation at work via morning commuting strain. In addition, the study examined the moderating roles of daily task significance, daily family interference with work, and commuting means efficacy in this mediation model. Results from 45 bus commuters’ daily diary data over a period of 15 workdays indicated that the amount of morning commuting stressors experienced by the bus commuters was positively related to their morning commuting strain, which, in turn, had a negative impact on self‐regulation at work. At the within‐person level, daily task significance buffered the negative indirect relationship between morning commuting stressors and self‐regulation at work via morning commuting strain, whereas daily family interference with work in the morning exacerbated this negative indirect relationship. Further, at the between‐person level, commuting means efficacy buffered this negative indirect relationship such that the negative indirect effect was weaker for workers with higher (vs. lower) commuting means efficacy. Theoretical and practical implications of these findings are discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".