Social Activities Do not Distract Everyone from Work A Diary Study of Work-Related Perseverative Cognition
Bibliographic record
Abstract
Work-related perseverative cognition (WPC) involves rumination about the past and worry about the future regarding workplace issues. Such cognition impedes workers’ daily recovery and well-being as it fosters prolonged activation of psychological stressors during leisure time. Considering these detrimental effects, it is important, for both theoretical and practical considerations to highlight coping strategies that individuals can use to reduce daily WPC. Previous studies have led to contradictory results regarding the potential of social activities to decrease daily WPC. The aim of this study was to bring new insights on these results by examining how the benefits from time spent on social activities (i.e., reducing WPC) vary according to an individual’s level of neuroticism. A total of 48 daytime workers from a Canadian university completed evening diaries on 10 days during two consecutive workweeks (316 data points). Participants recorded the number of minutes spent on social activities after each workday and the extent to which a series of WPC had crossed their mind during the evening. Results from Hierarchical Linear Modeling (HLM) analyses revealed that time spent on social activities was associated to a daily decrease of WPC for workers low in neuroticism but to an increase of WPC for those high in neuroticism. This study suggests that workers high in neuroticism may be less likely to benefit from social activities. The discussion focuses on why potentially protective mechanisms associated with social activities may not be helpful to them. Practical implications based on individuals’ level of neuroticism are offered.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".