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The study examined the factor structure of burnout, as measured with the Oldenburg Burnout Inventory. The participants were 235 employees of a public administration agency who assessed their burnout online for 10 consecutive working days. Two models were tested with multilevel confirmatory factor analysis, assuming the same one or two-factor structure at the within- and between-person levels. Both models showed a reasonable fit to the data, but due to a strong correlation between exhaustion and disengagement and low within-person reliability for disengagement, a unidimensional model seems more valid. A cross-level invariance was not confirmed for either of the structures, showing that factor loadings for the same items differ significantly between the levels. This suggests that burnout is not the same latent variable at each level; rather, there are factors other than daily burnout that influence person-level scores and ignoring these across-level discrepancies may lead to biased conclusions.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8121347 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0251257 | PLOS |
Alcohol Clin Exp Res (Hoboken)
September 2025
Department of Population and Public Health Sciences, University of Southern California, Los Angeles, California, USA.
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September 2025
Program in Physical Therapy and Department of Neurology, Washington University School of Medicine, St. Louis, Missouri, USA.
Introduction: An important frontier for neuropsychology involves developing additional technologies that could complement current behavioral approaches. Concurrent electroencephalographic (EEG) markers are especially promising for informing the neural processes underlying cognitive performance during neuropsychological assessments. The EEG aperiodic exponent shows sensitivity to both age and task-related effects, with prior studies relating smaller exponents to poorer performance in older adults, and larger exponents to greater task engagement in general.
View Article and Find Full Text PDFNpj Ment Health Res
September 2025
Department of Psychiatry and Psychotherapy, Charité - Universitätsmedizin Berlin (Campus Charité Mitte), Berlin, Germany.
Loneliness is a growing global health issue, yet real-time assessments of its objective risk and protective factors are limited. This study identifies momentary and daily predictors using digital phenotyping and temporal analysis. Analyzing 12788 momentary observations from social mobile sensing and actigraphy, we examined how they impact loneliness on average (between-person) and in daily fluctuations (within-person).
View Article and Find Full Text PDFFront Health Serv
August 2025
UCD Centre for Interdisciplinary Research, Education & Innovation in Health Systems, School of Nursing, Midwifery & Health Systems, University College Dublin, Dublin, Ireland.
Background: A failure to distinguish between person-centredness, person-centred care, and person-centred cultures can result in improvement initiatives focusing solely on improvement initiative metrics and outcomes, excluding the authentic experiences of patients and staff. Building on the foundational work of Dewing and McCormack, we have designed, piloted, and implemented the Person-centred Lean Six Sigma (PCLSS) model in public and private acute and community healthcare settings across Ireland. This model uses Lean Six Sigma, a widely adopted improvement methodology, through a person-centred lens with which improvement practitioners and healthcare staff can inspect their Lean Six Sigma practice and critically evaluate whether, to what extent, and how it is synergistic with person-centred approaches.
View Article and Find Full Text PDFFront Adolesc Med
March 2025
Department of Psychiatry, University of Illinois at Chicago.
Introduction: Cannabis and its main psychoactive constituent, delta-9-tetrahydrocannabinol (THC), are thought to weaken neurocognitive processes. However, past experimental research examining the acute effects of THC on neurocognition has produced mixed results. The current study aims to advance this literature through application of computational modeling and consideration of individual differences in cannabis use history and subjective drug effects.
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