Benefits of recovery after work among Turkish manufacturing managers and professionals
Bibliographic record
Abstract
Purpose A body of research evidence has shown that job stressors are associated with lower levels of satisfaction and psychological well‐being. It has been suggested that recovery after the work day may reduce fatigue, restore mood and improve well‐being. The purpose of this paper is to examine predictors and consequences of four recovery experiences (psychological detachment, relaxation, mastery, and control) identified by Sonnentag and Fritz, to replicate and extend their work. Design/methodology/approach Data were collected from 887 men and women managers and professionals working in the manufacturing sector in Turkey using anonymously completed questionnaires (a 58 percent response rate). Findings Respondents at higher organizational levels made more use of both mastery and control. Personality factors (need for achievement and workaholism components) were also positively correlated with use of mastery and control. Hierarchical regression analyses controlling both personal demographic and work situation characteristics showed generally positive relationships with use of recovery experiences and more favorable work and well‐being outcomes. Psychological detachment, however, was found to have negative relationships with some of these outcomes suggesting more complex relationships with use of this recovery experience. Research limitations/implications Questions of causality cannot be addressed since data were collected at only one point in time. Practical implications Individuals, through practice, and organizations, through training efforts, can encourage employees to practice recovery while off the job to improve their work satisfaction and individual well‐being. Originality/value The paper presents the first study of recovery experiences in Turkey.
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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 | 0.001 |
| 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".