Recovery after work experiences, employee well-being and intent to quit
Bibliographic record
Abstract
Purpose – A growing body of research suggests that psychological experiences related to recovery after work may reduce employee fatigue and exhaustion and improve well-being. The purpose of this paper is to extend this literature by examining several correlates and consequences of four recovery experiences: psychological detachment, relaxation, mastery, and control. Design/methodology/approach – Data were collected from 290 nursing staff working in hospitals using a questionnaire study and well-established measures. Hierarchical regression analyses were used to test the hypotheses. Findings – The results suggest that the four recovery experiences were, with one exception, positively and significantly correlated. Personal demographic variables (e.g. work status and level of education) had relationships with the use of particular recovery experiences. Passion was positively related to the use of mastery and control, while work intensity was negatively associated with the use of psychological detachment and relaxation. The use of particular recovery experiences was generally associated with lower intentions to quit and positive indicators of psychological well-being. Research limitations/implications – There are several implications for research and practice. Scholars can use the results to extend the theories such as the job demands-resources model, including the role of work intensity as job demands. At the organizational level, managers and leaders should consider supporting strategies that help employees recover after work. Originality/value – This study extends the empirical research on recovery after work using some variables not previously used. The theory on recovery after work is also extended.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.001 |
| 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".