scholarly journals Job Satisfaction among Health-Care Staff in Township Health Centers in Rural China: Results from a Latent Class Analysis

2017 ◽  
Vol 14 (10) ◽  
pp. 1101 ◽  
Author(s):  
Haipeng Wang ◽  
Chengxiang Tang ◽  
Shichao Zhao ◽  
Qingyue Meng ◽  
Xiaoyun Liu
Keyword(s):  
Job Satisfaction ◽  
Health Care ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Rural China ◽  
Health Centers ◽  
Care Staff ◽  
Health Care Staff ◽  
Class Analysis

scholarly journals Job Satisfaction and Associated Factors among Health Care Staff in Township Health Centers—— A Cross-Sectional Survey in Rural Central China

2020 ◽  
Author(s):  
Xuyu Chen ◽  
Hui Yao ◽  
Li Ran ◽  
Wenwen Wu ◽  
Yupeng Zhang ◽  
...  
Keyword(s):  
Job Satisfaction ◽  
Health Care ◽  
Brain Drain ◽  
Associated Factors ◽  
Educational Background ◽  
Health Centers ◽  
Central China ◽  
Care Staff ◽  
Health Care Staff ◽  
The Impact

Abstract Background:Township health centers play a cornerstone role in the work of primary health care in China while it’s development is largely limited by the brain drain. Job satisfaction is closely related to brain drain, investigating the relevant factors of job satisfaction can provide strategies to reduce brain drain. The aim of this study was to explore job satisfaction and associated factors among health-care staff in township health centers in Huangpi District. Methods: This research was conducted in Huangpi, China. Convenience sampling methods and self-administereded questionnaires were used. 1370 of valid samples were collected with 97.72% effective rate. Descriptive statistics are used to describe sociodemographic information. The Pearson Chi-square statistical was used to test the binary association between job satisfaction and another categorical variable. All the sociodemographic information was applied to the binary logistic regression model using the stepwise selection method. Results: The mean age was 36.98 (SD=9.84), Factors that affect job satisfaction include educational background (χ2= 7.99, p=0.046), monthly income (χ2= 51.43, p<0.001), hire form (χ2=7.64, p=0.049), hours worked per week (χ2=33.48, p<0.01), parent have had a stable job (χ2= 10.67, p<0.01). Conclusions: Government and management should consider the impact of current policies on job satisfaction to reduce staff’s personal job dissatisfaction, Reducing staff workload, increasing salary and overtime benefits, and promoting the fairness of wage distribution are potential strategies to ameliorate low levels of job satisfaction.

scholarly journals Measuring efficiency of federally qualified health centers : a multi model approach using data envelopment analysis (DEA) and latent class analysis (LCA)

2021 ◽  
Author(s):  
◽  
Rohith Madhi Reddy
Keyword(s):  
Health Care ◽  
Data Envelopment Analysis ◽  
Latent Class Analysis ◽  
Latent Class ◽  
Health Centers ◽  
Data Envelopment ◽  
Class Analysis ◽  
Federally Qualified Health Centers ◽  
Dea Model ◽  
Using Data

There are 1300 federally qualified health centers (FQHCs) in the United States providing the health care to underserved and uninsured population. These FQHCs serve the patients irrespective of their ability to pay. Using the resources effectively, these FQHCs can provide better health care. In this study of prenatal care, we measure the efficiencies of the FQHCs using data envelopment analysis (DEA). As in service industry, where quality is of at most importance, we used two different DEA approaches considering quality called the Two model DEA approach by (Shimshak, D., and Lenard, M.L.,2007) and Quality adjusted DEA approach by (Sherman, H.D., and Zhu, J, 2006). Efficient frontiers are determined by using these DEA approaches. There are differences that exists across FQHCs due to various factors to include demographic characteristics of patients visited the FQHCs, operational characteristics of health centers. Latent class analysis is performed before performing the DEA to classify the FQHCs into different classes based on the regional and population measures. Four different models namely aggregated Shimshak and Lenard and aggregated Sherman and Zhu models (DEA model is run on the whole sample), partitioned S and L and partitioned S and Z models (DEA model is run individually by class) have been used to determine the efficiencies of the FQHCs. Using the S and L approach, it is found that the FQHCs that formed the efficient frontier is of smaller FQHCs whereas the S and Z approach has a mix of small and large FQHCs. Based on the results determined, more insights are provided on the FQHCs and the models used in the analysis.

scholarly journals Associative stigma among mental health professionals in Singapore: a cross-sectional study

BMJ Open ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. e028179 ◽  
Author(s):  
Louisa Picco ◽  
Sherilyn Chang ◽  
Edimansyah Abdin ◽  
Boon Yiang Chua ◽  
Qi Yuan ◽  
...  
Keyword(s):  
Mental Health ◽  
Job Satisfaction ◽  
Latent Class Analysis ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Online Survey ◽  
Latent Class ◽  
Multinomial Logistic Regression ◽  
Class Analysis ◽  
Associative Stigma

Objectives(1) Investigate and explore whether different classes of associative stigma (the process by which a person experiences stigmatisation as a result of an association with another stigmatised person) could be identified using latent class analysis; (2) determine the sociodemographic and employment-related correlates of associative stigma and (3) examine the relationship between associative stigma and job satisfaction, among mental health professionals.DesignCross-sectional online survey.ParticipantsDoctors, nurses and allied health staff, working in Singapore.MethodsStaff (n=462) completed an online survey, which comprised 11 associative stigma items and also captured sociodemographic and job satisfaction-related information. Latent class analysis was used to classify associative stigma on patterns of observed categorical variables. Multinomial logistic regression was used to examine associations between sociodemographic and employment-related factors and the different classes, while multiple linear regression analyses were used to examine the relationship between associative stigma and job satisfaction.ResultsThe latent class analysis revealed that items formed a three-class model where the classes were classified as ‘no/low associative stigma’, ‘moderate associative stigma’ and ‘high associative stigma’. 48.7%, 40.5% and 10.8% of the population comprised no/low, moderate and high associative stigma classes, respectively. Multinomial logistic regression showed that years of service and occupation were significantly associated with moderate associative stigma, while factors associated with high associative stigma were education, ethnicity and occupation. Multiple linear regression analyses revealed that high associative stigma was significantly associated with lower job satisfaction scores.ConclusionAssociative stigma was not uncommon among mental health professionals and was associated with sociodemographic factors and poorer job satisfaction. Associative stigma has received comparatively little attention from empirical researchers and continued efforts to address this understudied yet important construct in conjunction with future efforts to dispel misconceptions related to mental illnesses are needed.

scholarly journals Working Atmosphere and Job Satisfaction of Health Care Staff in Kenya: An Exploratory Study

2015 ◽  
Vol 2015 ◽  
pp. 1-7 ◽  
Author(s):  
Katja Goetz ◽  
Michael Marx ◽  
Irmgard Marx ◽  
Marc Brodowski ◽  
Maureen Nafula ◽  
...  
Keyword(s):  
Job Satisfaction ◽  
Health Care ◽  
Health Care Professionals ◽  
Quality Management System ◽  
Linear Regression Analysis ◽  
Individual Characteristics ◽  
Care Staff ◽  
Health Care Staff ◽  
Good Opportunity ◽  
Overall Job Satisfaction

Background. Job satisfaction and working atmosphere are important for optimal health care delivery. The study aimed to document working atmosphere and job satisfaction of health care professionals in Kenya and to explore associations between job satisfaction, staff characteristics, and working atmosphere.Methods. Data from the integrated quality management system (IQMS) for the health sector in Kenya were used. Job satisfaction was measured with 10 items and with additional 5 items adapted to job situation in Kenya. Working atmosphere was measured with 13 item questionnaire. A stepwise linear regression analysis was performed with overall job satisfaction and working atmosphere, aspects of job satisfaction, and individual characteristics.Results. Out of 832 questionnaires handed out, 435 questionnaires were completed (response rate: 52.3%). Health care staff indicated high commitment to provide quality services and low levels regarding the adequacy and functionality of equipment at their work station. The aspect “support of the ministry of health” (β= 0.577) showed the highest score of explained variance (32.9%) regarding overall job satisfaction.Conclusions. IQMS which also evaluates job satisfaction and working atmosphere of health care staff provides a good opportunity for strengthening the recruitment and retention of health care staff as well as improving the provision of good quality of care.

scholarly journals Thinking About the End of Life When It Is Near: A Comparison of German and Portuguese Centenarians

2018 ◽  
Vol 41 (3) ◽  
pp. 265-285
Author(s):  
Kathrin Boerner ◽  
Daniela S. Jopp ◽  
Kyungmin Kim ◽  
Abigail Butt ◽  
Óscar Ribeiro ◽  
...  
Keyword(s):  
Health Care ◽  
Latent Class Analysis ◽  
End Of Life ◽  
Health Care Professionals ◽  
Latent Class ◽  
Individual Characteristics ◽  
Class Analysis ◽  
Class 1 ◽  
Afterlife Beliefs

This study examined how common thinking of and planning for the end of life (EOL) is among German and Portuguese centenarians, and whether patterns of EOL views are shaped by cultural and individual characteristics. A significant portion of centenarians in both countries reported not thinking about the EOL, not believing in the afterlife, and not having made EOL arrangements. Latent class analysis identified three EOL patterns: Class 1 ( EOL thoughts with EOL arrangements and afterlife beliefs), Class 2 ( EOL arrangements and afterlife beliefs without EOL thoughts), and Class 3 ( Overall low endorsement of EOL items). The proportion of Portuguese centenarians was higher in Class 1 and of German centenarians higher in Classes 2 and 3. Centenarians’ demographic, social, and health characteristics were significantly different across EOL patterns. As lack of EOL planning can result in poor EOL quality, enhancing communication among centenarians, family, and health-care professionals seems imperative.

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