Dataset Information

Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: A hospital-based cross-sectional study.

ABSTRACT: OBJECTIVES:To identify sociodemographic and illness-related factors associated with quality of life among people with Schizophrenia. METHODS:A hospital-based cross-sectional study design was employed among 351 people with schizophrenia and attending the followup service at Jimma University Medical Center, psychiatric clinic during the study period. Participants were recruited using a systematic random sampling method and a sample fraction of two was used after the first person was identified by a lottery method. Data entry was done using EpiData version 3.1 and then exported to Statistical Package for Social Sciences version 25 for analysis. Multiple regression analysis was used to determine the statistically significant association between quality of life and independent variables. RESULTS:Among the four domains of quality of life, respondents scored the lowest mean in the social relationships domain (10.14 ± 3.12). Final adjusted multiple regression model revealed, being divorced was negatively associated with the physical domain (? = -0.72, p = 0.02), having no formal education was negatively associated with physical health domain (? = -0.69, p = 0.001) and age was positively associated with the psychological domain (? = 0.371, p = 0.071). Being rural resident was negatively associated with physical domain (? = -0.48, p = 0.01), with environmental domain (? = -0.64, p = 0.03), with social relationships domain (? = -0.45, p = 0.04), and with overall quality of life (? = -1.93, p = 0.006). Positive symptoms (? = -0.22, p = 0.001), negative symptoms (? = -0.36, p = 0.001), and general psychopathology (? = -0.098, p = 0.006) were inversely associated with overall quality of life. CONCLUSION:In this study, the social relationship domain of quality of life among people with schizophrenia has the lowest mean score. Some socio-demographic variables and psychiatric symptoms were found to be key significant associated factors of quality of life. Priority interventions to improve the social deficits and addressing psychiatric symptoms of people with schizophrenia is essential to improve their quality of life.

SUBMITTER: Desalegn D

PROVIDER: S-EPMC7039432 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: A hospital-based cross-sectional study.

Desalegn Defaru D Girma Shimelis S Abdeta Tilahun T

PloS one 20200224 2

<h4>Objectives</h4>To identify sociodemographic and illness-related factors associated with quality of life among people with Schizophrenia.<h4>Methods</h4>A hospital-based cross-sectional study design was employed among 351 people with schizophrenia and attending the followup service at Jimma University Medical Center, psychiatric clinic during the study period. Participants were recruited using a systematic random sampling method and a sample fraction of two was used after the first person was ...[more]

PMID: 32092123

Similar Datasets

Project description:BackgroundPatient satisfaction is one proxy indicator of the health care quality; however, enhancing patient satisfaction in low-income settings is very challenging due to the inadequacy of resources as well as low health literacy among patients. In this study, we assess patient satisfaction and its correlates in a tertiary public hospital in Nepal.MethodsWe conducted a cross sectional study at outpatient department of Bhaktapur Hospital of Nepal. To recruit participants for the study, we applied a systematic random sampling method. Our study used a validated Patient Satisfaction Questionnaire III (PSQ-III) developed by RAND Corporation including various contextual socio-demographic characteristics. We calculated mean score and percentages of satisfaction across seven dimensions of patient satisfaction. To determine the association between various dimensions of patient satisfaction and socio-demographic characteristics of the patient, we used a multi-ordinal logistic regression.ResultsAmong 204 patients, we observed a wide variation in patient satisfaction across seven dimensions. About 39% of patients were satisfied in the dimension of general satisfaction, 92% in interpersonal manner, and 45% in accessibility and convenience. Sociodemographic factors such as age (AOR: 6.42; CI: 1.30-35.05), gender (AOR: 2.81; CI: 1.41-5.74), and ethnicity (AOR: 0.26; CI: 0.08-0.77) were associated with general satisfaction of the patients. Other sociodemographic variables such as education, occupation, and religion were associated with a majority of the dimensions of patient satisfaction (p < 0.05). Age was found to be the strongest predictor of patient satisfaction in five out of seven dimensions.ConclusionsWe concluded that patient satisfaction varies across different dimensions. Therefore, targeted interventions that direct to improve the dimensions of patient satisfaction where the proportion of satisfaction is low are needed. Similar studies should be conducted regularly at different levels of health facilities across the country to capture a wider picture of patient satisfaction at various levels.

Project description:BACKGROUND:Medical education is widely known to be a demanding process that may cause various mental health problems, such as burnout, which can lead to lowered levels of life satisfaction among medical students. Research shows that empathy is negatively correlated with burnout, but there are few studies on the relationship among empathy, burnout and life satisfaction in medical students. The objective of the present study is to explore the correlations of empathy and burnout with life satisfaction and the associated socio-demographic factors among Chinese undergraduate medical students. METHODS:In this cross-sectional study, 1271 undergraduate medical students (age 19.42 ± 1.34 years, 36% male) from 1st to 4th grades completed questionnaires including the Interpersonal Reactivity Index Chinese version (IRI-C), the Maslach Burnout Inventory Modified Chinese version (MBI-MC), the Satisfaction With Life Scale (SWLS) and socio-demographic characteristics. Statistical analyses included Student's t-test, one-way ANOVA, post hoc Bonferroni tests, hierarchical linear regression analysis and general linear model-univariate full factorial model. RESULTS:Over four academic years, medical students' empathy levels declined, but their burnout levels almost plateaued and their life satisfaction levels witnessed an initial fall before a rebound. Empathy was correlated with students' age and grade, and burnout was associated with students' maternal education. Significant differences in life satisfaction were detected with regard to medical students' age, academic year, the number of children in the family, place of residence and parents' educational levels. CONCLUSIONS:Empathy explained 0.6% of the variance in life satisfaction in contrast to 13.7% of the variance explained by burnout in life satisfaction. Although empathy did not have a main effect on life satisfaction, there was an interaction effect of empathy and burnout on life satisfaction among students of high and low empathy and burnout levels. Students with high levels of empathy and low levels of burnout were most satisfied with life. Medical institutions and related authorities need to find effective measures to enhance students' empathy levels and reduce burnout to improve their life satisfaction.

Project description:Questionnaires are among the most basic and widespread tools to assess the mental health of a population in epidemiological and public health studies. Their most obvious advantage (firsthand self-report) is also the source of their main problems: the raw data requires interpretation, and are a snapshot of the specific sample's status at a given time. Efforts to deal with both issues created a bi-dimensional space defined by two orthogonal axes, in which most of the quantitative mental health research can be located. Methods aimed to assure that mental health diagnoses are solidly grounded on existing raw data are part of the individual validity axis. Tools allowing the generalization of the results across the entire population compose the collective validity axis. This paper raises a different question. Since one goal of mental health assessments is to obtain results that can be generalized to some extent, an important question is how robust is a questionnaire result when applied to a different population or to the same population at a different time. In this case, there is deep uncertainty, without any a priori probabilistic information. The main claim of this paper is that this task requires the development of a new robustness to deep uncertainty axis, defining a three-dimensional research space. We demonstrate the analysis of deep uncertainty using the concept of robustness in info-gap decision theory. Based on data from questionnaires collected before and during the Covid-19 pandemic, we first locate a mental health assessment in the space defined by the individual validity axis and the collective validity axis. Then we develop a model of info-gap robustness to uncertainty in mental health assessment, showing how the robustness to deep uncertainty axis interacts with the other two axes, highlighting the contributions and the limitations of this approach. The ability to measure robustness to deep uncertainty in the mental health realm is important particularly in troubled and changing times. In this paper, we provide the basic methodological building blocks of the suggested approach using the outbreak of Covid-19 as a recent example.

Dataset Information

Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: A hospital-based cross-sectional study.

Publications

Quality of life and its association with psychiatric symptoms and socio-demographic characteristics among people with schizophrenia: A hospital-based cross-sectional study.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets