M Chehrazi, R Omani Samani , E Tehraninejad, H Chehrazi, A Arabipoor,
Volume 14, Issue 4 (Vol.14, No.4, 2019)
Abstract
Background and Objectives: Analysis of ordinal data outcomes could lead to bias estimates and large variance in sparse one. The objective of this study is to compare parameter estimates of an ordinal regression model under maximum likelihood and Bayesian framework with generalized Gibbs sampling. The models were used to analyze ovarian hyperstimulation syndrome data.
Methods: This study used the data from 138 patients of a clinical trial phase III to compare the efficacy of intravenous Albumin and Cabergoline in prevention of ovarian hyperstimulation syndrome. The original study was done between 2010 to 2011 in Royan institute. We compared maximum likelihood and Bayesian estimation with generalized Gibbs sampling for an ordinal regression model based on confidence intervals and standard errors. The model were fit through R 3.3.2 software version.
Results: Markov Chain Monte Carlo results reduced the standard errors for estimates and consequently, narrower confidence intervals. Autocorrelations for generalized Gibbs sampler reached to zero in compare to standard Gibbs sampler for shorter time.
Conclusion: It seems that confidence intervals of an ordinal regression model are shorter for generalized Gibbs sampler in compare to standard Gibbs and maximum likelihood. It suggests doing more studies to warrant the results.
Alireza Didarloo, Behrouz Fathi, Raana Hosseini, Habibollah Pirnejad, Sima Ghorbanzadeh, Kajal Yasamani,
Volume 19, Issue 1 (Vol.19, No.1, Spring 2023)
Abstract
Background and Objectives: Vaccination stands as a paramount achievement in global public health and a key strategy to control COVID-19. Vaccine acceptance is a pivotal determinant of the success or failure of vaccination programs. Leveraging health education models and theories to predict behavioral intention, this study aimed to investigate the determinants of the intention to receive the COVID-19 vaccine among the general population of Urmia using the Health Belief Model (HBM).
Methods: This descriptive-analytical study employed a cross-sectional approach among 575 individuals aged over 18 residing in Urmia. Sampling was conducted through the snowball and convenience sampling methods. Data was collected using a valid and reliable electronic researcher-made questionnaire comprising four sections: demographic characteristics, knowledge, HBM constructs, and intention to receive the COVID-19 vaccine. Data were analyzed using descriptive and inferential statistics in SPSS version 16.
Results: The HBM effectively explained 67% of the variance in the intention to vaccinate against COVID-19. Within the model's constructs, individuals' perceived self-efficacy (β = 0.505, P = 0.001) emerged as the strongest predictor of the intention to receive the COVID-19 vaccination. Other influencing factors included perceived susceptibility (β = 0.158, P = 0.001) and perceived barriers (β = -0.109, P = 0.001).
Conclusion: Given the robust predictive ability of the HBM for the intention to vaccinate against COVID-19, this model can be utilized in educational and behavioral programs and interventions. Special emphasis should be placed on effective constructs, particularly self-efficacy, to enhance citizens' willingness to receive the COVID-19 vaccine.
Parvaneh Isfahani, Mohammad Sarani, Somayeh Samani, Aliyeh Bazi, Seyedeh Masoumeh Hosseini Zare, Ahmad Siar Sadr, Maryam Sadat Hosseini, Seyedeh Mahboobeh Hosseini Zare,
Volume 20, Issue 2 (Vol.20, No.2, Summer 2024)
Abstract
Background and Objectives: Depression is one of the most prevalent mental disorders among students associated with a major decline in academic and social performance. This study was carried out to determine the prevalence of depression in Iran's nursing students.
Methods: the research was conducted as a systematic review and meta analysis, all published scientific articles related to the prevalence of depression in nursing students were searched in 5 databases (Web of Science, Scopus, PubMed, SID, Magiran) and Google Scholar search engine and then their quality was evaluated. The heterogeneity of the studies was investigated using the I2 index and meta-regression model to evaluate heterogeneity-prone variables at a significance level of 0.05. Ultimately, 9 articles met the criteria for inclusion in this study and were analyzed using Comprehensive Meta-Analysis (CMA) software.
Results: Based on the random model, the prevalence of depression in Iranian nursing students was equal to 3.2% (2.1 – 4.5; 95% confidence level). Results showed that the highest prevalence of depression in nursing students was 6.2% (5.3-7.1; 95% confidence limit) in Sistan and Balochestan province in 2004, while the lowest prevalence was 0.8% (0.5-1.2; confidence limit 95%) in Esfahan and Qom provinces in 2016. Also, there was a significant relationship between the calendar year, sample size, average age, and prevalence of depression in Iranian nursing students (P<0.05).
Conclusion: The results showed that the prevalence of depression in nursing students was 3.2%, which decreased with the increase of the calendar year and average age. Nevertheless, policymakers and managers must take measures to reduce depression.
