Dataset Information

Burnout as a State: Random-Intercept Cross-Lagged Relationship Between Exhaustion and Disengagement in a 10-Day Study.

ABSTRACT: Background:Burnout has been traditionally seen as a chronic and stable state in response to prolonged stress. However, measures of momentary burnout are not well established, even though the within-person approach suggests that the symptoms of burnout may vary from day to day for the same employee. The aim of this study is to examine the daily inter- and intra-personal variability of the symptoms of burnout and the cross-lagged relationship between two components of burnout, exhaustion and disengagement. Methods:An online diary study over 10 consecutive workdays was conducted among 235 civil servants (75% women, average tenure of 15 years). Daily burnout was measured with the eight-item Oldenburg Burnout Inventory. Results:The intra-class correlation coefficients indicate that, although significant between-person variability exists, most of the burnout variance is within persons. Using the random intercept cross-lagged panel (RI-CLP) model to control for these between-person differences, mainly insignificant "pure" within-person cross-lagged relationships between exhaustion and disengagement were revealed. Moreover, day-to-day autoregressive effects were weaker than same-day residual correlations. Conclusion:This is one of the first studies to use daily diaries and the RI-CLP model to study burnout, including the separation of the more stable and more dynamic parts of each component. When stable parts were controlled for, the same-day relationships between exhaustion and disengagement were more pronounced than day-to-day effects. This might suggest stronger situational influences than carryover mechanism. Thus, conceptualizing burnout in terms of daily symptoms may shed promising insights into how it develops and add implications for pro-healthy changes in the workplace.

SUBMITTER: Basinska BA

PROVIDER: S-EPMC7084126 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Burnout as a State: Random-Intercept Cross-Lagged Relationship Between Exhaustion and Disengagement in a 10-Day Study.

Basinska Beata A BA Gruszczynska Ewa E

Psychology research and behavior management 20200316

<h4>Background</h4>Burnout has been traditionally seen as a chronic and stable state in response to prolonged stress. However, measures of momentary burnout are not well established, even though the within-person approach suggests that the symptoms of burnout may vary from day to day for the same employee. The aim of this study is to examine the daily inter- and intra-personal variability of the symptoms of burnout and the cross-lagged relationship between two components of burnout, exhaustion a ...[more]

PMID: 32214858

Similar Datasets

Project description:BackgroundWhen study data are clustered, standard regression analysis is considered inappropriate and analytical techniques for clustered data need to be used. For prediction research in which the interest of predictor effects is on the patient level, random effect regression models are probably preferred over standard regression analysis. It is well known that the random effect parameter estimates and the standard logistic regression parameter estimates are different. Here, we compared random effect and standard logistic regression models for their ability to provide accurate predictions.MethodsUsing an empirical study on 1642 surgical patients at risk of postoperative nausea and vomiting, who were treated by one of 19 anesthesiologists (clusters), we developed prognostic models either with standard or random intercept logistic regression. External validity of these models was assessed in new patients from other anesthesiologists. We supported our results with simulation studies using intra-class correlation coefficients (ICC) of 5%, 15%, or 30%. Standard performance measures and measures adapted for the clustered data structure were estimated.ResultsThe model developed with random effect analysis showed better discrimination than the standard approach, if the cluster effects were used for risk prediction (standard c-index of 0.69 versus 0.66). In the external validation set, both models showed similar discrimination (standard c-index 0.68 versus 0.67). The simulation study confirmed these results. For datasets with a high ICC (≥15%), model calibration was only adequate in external subjects, if the used performance measure assumed the same data structure as the model development method: standard calibration measures showed good calibration for the standard developed model, calibration measures adapting the clustered data structure showed good calibration for the prediction model with random intercept.ConclusionThe models with random intercept discriminate better than the standard model only if the cluster effect is used for predictions. The prediction model with random intercept had good calibration within clusters.

Dataset Information

Burnout as a State: Random-Intercept Cross-Lagged Relationship Between Exhaustion and Disengagement in a 10-Day Study.

Publications

Burnout as a State: Random-Intercept Cross-Lagged Relationship Between Exhaustion and Disengagement in a 10-Day Study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets