Work-related burnout among personnel at a university hospital: identifying quantitative and qualitative differences using latent class analysis

How to measure bullying at work? A latent class analysis of the Negative Acts Questionnaire How to measure bullying at work? A latent class analysis of the Negative Acts Questionnaire Guy Notelaers , Hans De Witte , Jeroen Vermunt & Stale Einarsen,Gedrag & Organisatie, Volume 19, Juni 2006, nr. 2, pp. 140. Bullying at work can be defined as a gradually escalating process. The actual measurements of bullying, however, do not adequately measure this process, and show several methodological and substantive shortcomings. In this study, a latent class analysis is performed on data (N = 6175) gathered with the Negative Acts Questionnaire ('objective' measurement of bullying). Six clusters can be distinguished: those 'not bullied' (35,3%), the 'limited work criticism'-cluster (27,7%), those with 'limited negative encounters' (16,5%), the 'sometimes bullied' (9%), the 'work related bullied' (8,3%) and the 'victims' (3,2%). These results suggest a cumulative measurement model for bullying at work, in which the type of negative behaviours and their intensity gradually increase. The results of this latent class analysis fit the definition of bullying at work in which such a gradual escalation is described. The size of the victims group also fits the size mentioned in the international literature, when bullying is measured with a subjective method.

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Intimate partner violence against low-income women in Mexico City and associations with work-related disruptions: a latent class analysis using cross-sectional data

Journal of Epidemiology & Community Health ◽

10.1136/jech-2017-209681 ◽

2018 ◽

Vol 72 (7) ◽

pp. 605-610 ◽

Cited By ~ 7

Author(s):

Jhumka Gupta ◽

Tiara C Willie ◽

Courtney Harris ◽

Paola Abril Campos ◽

Kathryn L Falb ◽

...

Keyword(s):

Intimate Partner Violence ◽

Latent Class Analysis ◽

Sexual Violence ◽

Mexico City ◽

Low Income ◽

Partner Violence ◽

Latent Class ◽

Intimate Partner ◽

Class Analysis ◽

Work Related

BackgroundDisrupting women’s employment is a strategy that abusive partners could use to prevent women from maintaining economic independence and stability. Yet, few studies have investigated disruptions in employment among victims of intimate partner violence (IPV) in low-income and middle-income countries. Moreover, even fewer have sought to identify which female victims of IPV are most vulnerable to such disruptions.MethodsUsing baseline data from 947 women in Mexico City enrolled in a randomised controlled trial, multilevel latent class analysis (LCA) was used to classify women based on their reported IPV experiences. Furthermore, multilevel logistic regression analyses were performed on a subsample of women reporting current work (n=572) to investigate associations between LCA membership and IPV-related employment disruptions.ResultsOverall, 40.6% of women who were working at the time of the survey reported some form of work-related disruption due to IPV. LCA identified four distinct classes of IPV experiences: Low Physical and Sexual Violence (39.1%); High Sexual and Low Physical Violence class (9.6%); High Physical and Low Sexual Violence and Injuries (36.5%); High Physical and Sexual Violence and Injuries (14.8%). Compared with women in the Low Physical and Sexual Violence class, women in the High Physical and Sexual Violence and Injuries class and women in the High Physical and Low Sexual Violence and Injuries class were at greater risk of work disruption (adjusted relative risk (ARR) 2.44, 95% CI 1.80 to 3.29; ARR 2.05, 95% CI 1.56 to 2.70, respectively). No other statistically significant associations emerged.ConclusionIPV, and specific patterns of IPV experiences, must be considered both in work settings and, more broadly, by economic development programmes.Trial registration numberNCT01661504.

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Patterns of health service use following work‐related injury and illness in Australian truck drivers: A latent class analysis

American Journal of Industrial Medicine ◽

10.1002/ajim.23072 ◽

2019 ◽

Vol 63 (2) ◽

pp. 180-187

Author(s):

Ting Xia ◽

Ross Iles ◽

Sharon Newnam ◽

Dan I. Lubman ◽

Alex Collie

Keyword(s):

Health Service ◽

Latent Class Analysis ◽

Service Use ◽

Latent Class ◽

Truck Drivers ◽

Health Service Use ◽

Class Analysis ◽

Work Related ◽

Work Related Injury

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Characteristics of Super-Utilizers of Care at the University Hospitals of Geneva using Latent Class Analysis

10.5121/csit.2021.111801 ◽

2021 ◽

Author(s):

Gilles Cohen ◽

Pascal Briot ◽

Pierre Chopard

Keyword(s):

Health Care ◽

Latent Class Analysis ◽

Latent Class ◽

Clinical Information ◽

Patient Characteristics ◽

University Hospital ◽

Significant Heterogeneity ◽

Class Analysis ◽

University Hospitals ◽

The University

In hospitalized populations, there is significant heterogeneity in patient characteristics, disease severity, and treatment responses, which generally translates into very different related outcomes and costs. A better understanding of this heterogeneity can lead to better management, more effective and efficient treatments by personalizing care to better meet patients' profiles. Thus, identifying distinct clinical profiles among patients can lead to more homogenous subgroups of patients. Super-utilizers (SUs) are such a group, who contribute a substantial proportion of health care costs and utilize a disproportionately high share of health care resources. This study uses cost, utilization metrics and clinical information to segment the population of patients (N=32,759) admitted to the University Hospital of Geneva in 2019 and thus identifies the characteristics of its SUs group using Latent Class Analysis. This study shows how cluster analysis might be valuable to hospitals for identifying super-utilizers within their patient population and understanding their characteristics.

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