A latent profile analysis of doctors' joy in work at public hospitals

Introduction

When doctors' work stress increases, their joy in work decreases, severely affecting the quality of care and threatening patient safety. Analysis of the latent categories of joy in work of doctors in public hospitals and the differences in the characteristics of each category can help uncover hidden messages that enhance doctors' joy in work.

Methods

Questionnaires were administered to 426 doctors working in public hospitals using the general information questionnaire and the public hospital doctor's joy in work evaluation scale. Upon identifying their potential categories using latent profile analysis, chi-square test, and multinomial logistic regression were performed to analyze the differences in the characteristics of each category.

Results

The 426 public hospital doctors could be divided into three potential categories: "low joy in work" (11.27%), "medium joy in work" (59.86%), and "high joy in work" (28.87%). Most of the doctors did not have much joy in work, with 71.13% of them having "low to medium joy in work." Doctors who work in secondary or tertiary hospitals, have a personnel agency or contract, and are older than 45 years are more likely to belong to the "low joy in work" category. Some of the protective factors are having an average monthly income (RMB) of 10,001-15,000 yuan and having a fair or good self-rated health status.

Conclusion

There are obvious classification characteristics of doctors' level of joy in work. Hospital managers can take commensurate actions to improve their joy in work, thereby improving patient safety and the quality of medical services.

Patterns of Women's exposure to psychological violence: A global examination of low- and middle-income countries

Introduction

Under Sustainable Development Goal 5, prevalence of intimate partner violence (IPV) is a globally reportable indicator. There is a lack of consensus on how to measure and report psychological IPV, affecting prevalence estimates and cross-country comparability. We examine similarities and differences in the patterning of women's experiences of psychological abuse in low- and middle-income countries (LMICs) to inform common cut points.

Methods

Data include 13,452 ever-partnered women from six LMICs participating in the WHO multi-country study on women's health and domestic violence against women and 306,101 from 47 LMICs participating in the Demographic and Health Surveys. A confirmatory latent class analysis (LCA) approach was applied to identify the optimal class structure using the 3 DHS and 4 WHO psychological IPV items, assessed the impact of physical and sexual IPV on class structure, and tested class generalizability across countries. We validated the three-class solution by regressing the classes on physical IPV, sexual IPV, controlling behaviors, and injury due to domestic violence. We used item response theory (IRT) methods to assess item-level characteristics of the items.

Results

Analysis confirmed the three-class structure in most countries. Addition of physical and sexual IPV did not change overall class structure or improve discrimination or homogeneity of the items. The three-class structure was invariant within most WHO-classified regions. Operationalized classes informed by the LCA resulted in prevalences of roughly 90% low-to-no class, 7% moderate-intensity class, and 3% high-intensity class. Classes showed convergent validity with all outcomes tested. IRT analysis revealed good discriminations but substantial information overlaps over a narrow range of the latent psychological violence construct.

Conclusions

This study confirms the three-class pattern but suggests some differences across countries. and regions. We suggest cut points distinguishing violent from non-violent acts and demarcating levels of severity for future study. Findings offer evidence-based guidance to rectify challenges.

An in-depth look at latent classes of DSM-5 psychiatric comorbidity among individuals with PTSD: Clinical indicators and treatment utilization

OBJECTIVE

Posttraumatic stress disorder (PTSD) is associated with high comorbidity rates across the full range of psychiatric disorders. However, little is known about how psychiatric comorbidity manifests among people with PTSD, particularly with regard to concurrent diagnoses.

METHOD

Latent class analysis (LCA) was used to characterize discrete classes of PTSD comorbidity using past year DSM-5 diagnostic standards among a large nationally representative epidemiologic sample of U.S. adults. Follow-up analyses compared participant characteristics across latent classes.

RESULTS

The LCA was best characterized by five classes: low comorbidity, distress-fear, distress-externalizing, mania-fear-externalizing, and mania-externalizing. Excluding the low comorbidity class, proportions of borderline and schizotypal personality disorder were high across classes.

CONCLUSION

Participant characteristics across classes of past year PTSD comorbidity are explored through the lens of case conceptualization and treatment planning utility.

A Latent Profile Analysis of Online Dating Patterns Among Single Young Men Who Have Sex with Men

Despite the increased use of geosocial networking applications for finding sexual partners among young men who have sex with men (YMSM), few studies have examined the intricate patterns of online dating behaviors. In order to advance understanding of online dating patterns among YMSM, various factors, including how frequently and how much time is spent within partner-seeking sites, need to be examined concurrently. Therefore, we used latent profile analysis to identify online dating patterns and logistic regressions to examine their associations with sexual behaviors and relationship characteristics among single YMSM (N = 180; ages 18-24). We found three online dating patterns: discouraged users (N = 93, 52%), date seekers (N = 67, 37%), and instant lovers (N = 20, 11%). Discouraged users were less likely to seek sexual sensational activities, while date seekers were more likely to seek sexual sensational activities. Moreover, instant lovers were less likely to pursue committed romantic relationships, while they reported a higher number of condomless anal intercourse. Given that online dating patterns are not homogeneous, HIV prevention interventions may benefit from tailored approaches based on YMSM's different online dating profiles.

Conceptualizing and Measuring Appetite Self-Regulation Phenotypes and Trajectories in Childhood: A Review of Person-Centered Strategies

This review uses person-centered research and data analysis strategies to discuss the conceptualization and measurement of appetite self-regulation (ASR) phenotypes and trajectories in childhood (from infancy to about ages 6 or 7 years). Research that is person-centered provides strategies that increase the possibilities for investigating ASR phenotypes. We first examine the utility of examining underlying phenotypes using latent profile/class analysis drawing on cross-sectional data. The use of trajectory analysis to investigate developmental change is then discussed, with attention to phenotypes using trajectories of individual behaviors as well as phenotypes based on multi-trajectory modeling. Data analysis strategies and measurement approaches from recent examples of these person-centered approaches to the conceptualization and investigation of appetite self-regulation and its development in childhood are examined. Where relevant, examples from older children as well as developmental, clinical and educational psychology are drawn on to discuss when and how person-centered approaches can be used. We argue that there is scope to incorporate recent advances in biological and psychoneurological knowledge about appetite self-regulation as well as fundamental processes in the development of general self-regulation to enhance the examination of phenotypes and their trajectories across childhood (and beyond). The discussion and conclusion suggest directions for future research and highlight the potential of person-centered approaches to progress knowledge about the development of appetite self-regulation in childhood.

Latent classes of aggression and peer victimization: Measurement invariance and differential item functioning across sex, race-ethnicity, cohort, and study site

Peer victimization is common and linked to maladjustment. Prior research has typically identified four peer victimization subgroups: aggressors, victims, aggressive‐victims, and uninvolved. However, findings related to sex and racial‐ethnic differences in subgroup membership have been mixed. Using data collected in September of 2002 and 2003, this study conducted confirmatory latent class analysis of a racially‐ethnically diverse sample of 5415 sixth graders (49% boys; 50.6% Black; 20.9% Hispanic) representing two cohorts from 37 schools in four U.S. communities to replicate the four subgroups and evaluate measurement invariance of latent class indicators across cohort, sex, race‐ethnicity, and study site. Results replicated the four‐class solution and illustrated that sociodemographic differences in subgroup membership were less evident after accounting for differential item functioning.

Confirming Profiles of Comorbid Psychological Symptoms in Urban Youth: Exploring Gender Differences and Trait Mindfulness

Prior work has identified the need for replication of psychological research; however, validation efforts are rare. The purpose of the current study was to confirm latent profiles of comorbid psychological symptoms in an urban adolescent sample and examine differences in gender and trait mindfulness across these profiles. Cross-sectional data from 201 eighth grade students (63% female; M_age = 13.24; 86% Black) across nine Baltimore City public middle schools were analyzed. Confirmatory latent profile analyses showed that the previously-identified 3-profile solution with boundary constraints was the best fit for the data, and significant sex and trait mindfulness differences were identified. The current study supports the need for future replication studies using this methodology to improve theory and targeted interventions.

Classes of lifetime adversity in emerging adult women and men and their associations with weight status

The aim of this paper was to better understand how child and adult adversities cluster together into classes, and how these classes relate to body weight and obesity. Analyses included 2015 and 2018 data from emerging adults (18-25 years old) who participated in a state surveillance system of 2- and 4-year college students in Minnesota (N = 7475 in 2015 and N = 6683 in 2018). Latent Class Analyses (LCA) of 12 child and adult adversities were run stratified by gender and replicated between 2015 and 2018. The distal outcome procedure and three-step Bolck-Croon-Hagenaars approach were used to estimate predicted BMI means and predicted probabilities of obesity for each class, adjusted for covariates. The LCA identified seven classes in women and 5 in men. In women, BMI ranged from 23.9 kg/m2 in the lowest-BMI class ("Adult Adversities and Childhood Household Dysfunction"; 95% CI: 22.6-25.1) to 27.3 kg/m2 in the highest-BMI class ("High Lifetime Adversities"; 95% CI: 25.9-28.7), a statistically significant difference of 3.4 kg/m2. In men, the adjusted BMIs ranged from 24.6 kg/m2 ("Low Adversities"; 95% CI: 24.3-25.0) to 26.0 kg/m2 ("Childhood Household Mental Illness"; 95% CI: 25.1-26.9), a statistically significant difference of 1.4 kg/m2. The pattern was similar for obesity. These results indicate that specific classes of child and adult adversities are strongly associated with BMI and obesity, particularly in women. A key contribution of LCA appeared to be identification of small classes at high risk for excess weight.

Identifying Student Subgroups as a Function of School Level Attributes: A Multilevel Latent Class Analysis

The purpose of the present study was to profile high school students’ achievement as a function of their demographic characteristics, parent attributes (e.g., education), and school behaviors (e.g., number of absences). Students were nested within schools in the Saudi Arabia Kingdom. Out of a large sample of 500k, participants involved 3 random samples of 2,000 students measured during the years 2016, 2017, and 2018. Randomization was conducted at the student level to ensure that all school units will be represented and at their respective frequency. Students were nested within 50 high schools. We adopted the multilevel latent profile analysis protocol put forth by Schmiege et al. (2018) and Mäkikangas et al. (2018) that account for nested data and tested latent class structure invariance over time. Results pointed to the presence of a 4-profile solution based on BIC, the Bayes factor, and several information criteria put forth by Masyn (2013). Latent profile separation was mostly guided by parents’ education and the number of student absences (being positive and negative predictors of high achievement classes, respectively). Two models tested whether the proportions of level 1 profiles to level 2 units are variable and whether level 2 profiles vary as a function of level 1 profiles. Results pointed to the presence of significant variability due to schools.

Latent profile transition analyses and growth mixture models: A very non-technical guide for researchers in child and adolescent development

Developmental scientists are often interested in subgroups of people who share commonalities in aspects of development; these subgroups often cannot be captured directly but instead must be inferred from other information. Mixture models can be used in these situations. Two specific types of mixture models, latent profile transition analyses and growth mixture models, are highly relevant to developmental science because they can identify subgroups of people who are similar in their patterns of change. This guide highlights foundational aspects of these two types of models and is intended for readers who have not previously conducted either an LPTA or a GMM, or perhaps no mixture model analyses at all. It includes four primary sections. The first focuses on understanding mixture models conceptually and applying that knowledge to identifying appropriate research questions. The second section addresses data requirements, including planning for data collection or evaluating the suitability of previously collected data, and data preparation. The third section focuses on conducting analyses, with step-by-step instructions and syntax, and the final section discusses presenting the results. I illustrate these concepts and procedures with an example data set and research questions derived from the Five Cs model of positive youth development.

Regularized Latent Class Analysis for Polytomous Item Responses: An Application to SPM-LS Data

The last series of Raven's standard progressive matrices (SPM-LS) test was studied with respect to its psychometric properties in a series of recent papers. In this paper, the SPM-LS dataset is analyzed with regularized latent class models (RLCMs). For dichotomous item response data, an alternative estimation approach based on fused regularization for RLCMs is proposed. For polytomous item responses, different alternative fused regularization penalties are presented. The usefulness of the proposed methods is demonstrated in a simulated data illustration and for the SPM-LS dataset. For the SPM-LS dataset, it turned out the regularized latent class model resulted in five partially ordered latent classes. In total, three out of five latent classes are ordered for all items. For the remaining two classes, violations for two and three items were found, respectively, which can be interpreted as a kind of latent differential item functioning.

An examination of help-seeking preferences via best-worst scaling

OBJECTIVE

This study utilized best-worst scaling and latent class analysis to assess mental health treatment preferences and identify subgroups of college student help seekers.

METHOD

College students (N = 504; age: M = 20.3, 79.2% female) completed assessments of mental health treatment preferences, self-stigma, and distress.

RESULTS

Students preferred utilizing friends and family, followed by professional mental health providers, self-help, keeping concerns to themselves, physicians, and lastly religious leaders. Latent class analyses identified four classes of respondents.

CONCLUSIONS

Subgroups of student help seekers include Formal Help Seekers who prefer professional mental health providers, Informal Help Seekers who prefer friends and family, Ambivalent Help Seekers who prefer family and friends but also keeping concerns to themselves, and Help Avoiders who prefer keeping concerns to themselves. Assessing treatment preferences among different student subgroups may constitute an initial step in identifying effective ways to address university-wide mental health concerns.

Using Outliers for Theory Building

Outliers are promising candidates for theory building because they defy expected cause-and-effect relationships. Nonetheless, researchers often treat them as a nuisance and exclude them from further study. In fact, our analysis founds only two article using outliers for theory development in all quantitative articles published from 1993 to 2012 in six major management journals, and less than 5% cared to even mention them (relaying reasons for deleting them, mostly). To rectify this, we provide a roadmap for empirical researchers interested in theory building.

The Application of Latent Class Analysis for Investigating Population Child Mental Health: A Systematic Review

Background: Latent class analysis (LCA) can be used to identify subgroups of children with similar patterns of mental health symptoms and/or strengths. The method is becoming more commonly used in child mental health research, but there are reservations about the replicability, reliability, and validity of findings. Objective: A systematic literature review was conducted to investigate the extent to which LCA has been used to study population mental health in children, and whether replicable, reliable and valid findings have been demonstrated. Methods: Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines were followed. A search of literature, published between January 1998 and December 2017, was carried out using MEDLINE, EMBASE, PsycInfo, Scopus, ERIC, ASSIA, and Google Scholar. A total of 2,748 studies were initially identified, of which 23 were eligible for review. The review examined the methods which studies had used to choose the number of mental health classes, the classes that they found, and whether there was evidence for the validity and reliability of the classes. Results: Reviewed studies used LCA to investigate both disparate mental health symptoms, and those associated with specific disorders. The corpus of studies using similar indicators was small. Differences in the criteria used to select the final LCA model were found between studies. All studies found meaningful or useful subgroups, but there were differences in the extent to which the validity and reliability of classes were explicitly demonstrated. Conclusions : LCA is a useful tool for studying and classifying child mental health at the population level. Recommendations are made to improve the application and reporting of LCA and to increase confidence in findings in the future, including use of a range of indices and criteria when enumerating classes, clear reporting of methods for replicability, and making efforts to establish the validity and reliability of identified classes.

Subtypes Based on Neuropsychological Performance Predict Incident Dementia: Findings from the Rush Memory and Aging Project

BACKGROUND

In a previous report, we used latent class analysis (LCA) to identify natural subgroups of older adults in the Einstein Aging Study (EAS) based on neuropsychological performance. These subgroups differed in demographics, genetic profile, and prognosis. Herein, we assess the generalizability of these findings to an independent sample, the Rush Memory and Aging Project (MAP), which used an overlapping, but distinct neuropsychological battery.

OBJECTIVE

Our aim was to identify the association of natural subgroups based on neuropsychological performance in the MAP cohort with incident dementia and compare them with the associations identified in the EAS.

METHODS

MAP is a community-dwelling cohort of older adults living in the northeastern Illinois, Chicago. Latent class models were applied to baseline scores of 10 neuropsychological measures across 1,662 dementia-free MAP participants. Results were compared to prior findings from the EAS.

RESULTS

LCA resulted in a 5-class model: Mixed-Domain Impairment (n = 71, 4.3%), Memory-specific-Impairment (n = 274, 16.5%), Average (n = 767, 46.1%), Frontal Impairment (n = 222, 13.4%), and a class of Superior Cognition (n = 328, 19.7%). Similar to the EAS, the Mixed-Domain Impairment, the Memory-Specific Impairment, and the Frontal Impairment classes had higher risk of incident Alzheimer's disease when compared to the Average class. By contrast, the Superior Cognition had a lower risk of Alzheimer's disease when compared to the Average class.

CONCLUSIONS

Natural cognitive subgroups in MAP are similar to those identified in EAS. These similarities, despite study differences in geography, sampling strategy, and cognitive tests, suggest that LCA is capable of identifying classes that are not limited to a single sample or a set of cognitive tests.

Person-Centered Methodologies in the Organizational Sciences

The 2011 Organizational Research Methods Feature Topic on latent class procedures has helped to establish person-centered analyses as a method of choice in the organizational sciences. This establishment has contributed to the generation of substantive-methodological synergies leading to a better understanding of a variety of organizational phenomena and to an improvement in research methodologies. The present Feature Topic aims to provide a user-friendly introduction to these new methodological developments for applied organizational researchers. Organized around a presentation of the typological, prototypical, and methodologically exploratory nature of person-centered analyses, this introductory article introduces seven contributions aiming to: (a) clarify the meaning, advantages, and applications of person-centered analyses; (b) illustrate emerging prototypical and longitudinal cluster analytic approaches; (c) introduce researchers to multilevel person-centered analyses as well as to auxiliary approaches that will drastically increase the scope of application of these methods; and (d) describe the application of these methods for confirmatory purposes.