Longitudinal association between psychological demands and burnout for employees experiencing a high versus a low degree of job resources
Bibliographic record
Abstract
BACKGROUND: Exhaustion and burnout are common causes for sickness absence. This study examines the relationship between psychological demands and burnout over time, and if environmental support modifies the longitudinal relationship between psychological demands and burnout at baseline, with burnout measured 2 years subsequently. METHODS: A questionnaire was sent to employees in seven Swedish organizations in 2010-2012 with follow-up after 2 years, n = 1722 responded (64%). Linear regressions were used to examine the associations between burnout and psychological demands at baseline and burnout at follow-up. Stratified regression models examined if relationships between burnout and psychological demands at baseline on burnout at follow-up differed for employees in supportive versus unsupportive work environments. RESULTS: Burnout and psychological demands at baseline were associated with burnout at follow-up, after adjustment for study covariates. No significant differences were observed between estimates for psychological demands and burnout among respondents in supportive work environments versus those in unsupportive work environments. CONCLUSIONS: This study shows that high demands are associated with greater risk of burnout, regardless of level of other work supports. This has implications for prevention of sick leave due to burnout and for rehabilitation, where demands such as work pace, workload and conflicting demands at work may need to be reduced.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".