Emotional exhaustion across the workday: Person-level and day-level predictors of workday emotional exhaustion growth curves.
Bibliographic record
Abstract
Despite empirical findings that have established the dynamic nature of emotional exhaustion (EE), the temporal processes underlying the development of EE over meaningful spans of time have largely been ignored in research. Drawing from theories that outline the roles of resources and demands at work (Demerouti et al., 2001; Halbesleben et al., 2014; Hobfoll, 1989; ten Brummelhuis & Bakker, 2012), the present study developed and tested hypotheses pertaining to the form and predictors of workday EE trajectories. Experience sampling methodology was utilized to assess the momentary EE of 114 employees three times per day over a total of 925 days and 2,808 event-level surveys. Within-day EE growth curves (i.e., intercepts and slopes) were then derived, and the variance of these growth curve terms was partitioned into within-person (i.e., variance in growth curve parameters across days for each person) and between-person (i.e., variance in average growth curve parameters across people) sources. Results supported an increasing pattern of EE across the workday and also demonstrated substantial between- and within-person variance in intercepts (i.e., start) and slopes (i.e., growth) over the workday. In addition, support was found for a set of resource-providing and resource-consuming predictors of EE growth curves, including customer mistreatment, social interactions with coworkers, prior evening psychological detachment, perceived supervisor support, and autonomous and controlled motivations for one's job. (PsycInfo Database Record (c) 2025 APA, all rights reserved).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 teacher head, 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".