A Multilevel Analysis of Changes in Psychological Demands over Time on Employee Burnout
Bibliographic record
Abstract
In pursuing this study, we were interested in the effect of changes in psychological demands over time on burnout. We were also interested in examining the moderating role resources could play between changes in job demands over time and employee burnout. Multilevel regression analyses of repeated measures were conducted to capture the hierarchical structure of the data (time (Level 1, n = 537 (12-month period between T1 and T2)); employees (Level 2, n = 289)) nested in firms (Level 3, n = 34). To measure change in psychological demands, the distribution of psychological demands at T1 and T2 were dichotomized at the T1 median. Following this dichotomization, four groups were created: low T1 and low T2; high T1 and low T2; low T1 and high T2, high T1 and high T2. In terms of direct associations, an increase in psychological demands over time was associated with emotional exhaustion and cynicism but not professional efficacy. Locus of control, self-esteem, and social support from supervisors were also directly associated with burnout. As for interaction effects, social support from coworkers attenuated the effect of changes in psychological demands over time (i.e., increasing psychological demands) on cynicism. In other words, employees facing greater psychological demands over time (increasing psychological demands) and benefitting from social support from their coworkers had less cynicism. Our findings offer meaningful insights into possible ways of lowering burnout levels. Based on the results obtained, psychological demands, social support, locus of control, and self-esteem should be considered valuable intervention targets.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".