Better together? Examining profiles of employee recovery experiences.
Bibliographic record
Abstract
Employees are exposed to a wide variety of job demands that deplete personal resources and necessitate recovery. In light of this need, research on work recovery has focused on how distinct recovery experiences during postwork time relate to employee well-being. However, investigators have largely tested the effects of these experiences in isolation, neglecting the possibility that profiles of recovery experiences may exist and influence the recovery process. The current set of studies adopted a person-centered approach using latent profile analysis to understand whether unique constellations of recovery experiences-psychological detachment, relaxation, mastery, control, and problem-solving pondering-emerged for 2 samples of full-time employees. In Study 1, which involved a single-time-point assessment, we identified 4 unique profiles of recovery experiences, tested whether job demands (i.e., time pressure, role ambiguity) and job resources (i.e., job control) differentiated profile membership, and evaluated whether each profile uniquely related to employee well-being outcomes (i.e., emotional exhaustion, engagement, somatic complaints). In Study 2, which involved 2 time points, we replicated 3 of the 4 profiles observed in Study 1, and tested 2 additional antecedents rated by employees' supervisors: leader-member exchange and supervisor support for recovery. Across both studies, unique differences emerged in regard to antecedents and outcomes tied to recovery experience profile membership. (PsycINFO Database Record
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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".