Professional quality of life and its associated factors among Vietnamese doctors and nurses.

BACKGROUND: Prevalence of health workers with occupational health issues ranked fourth among all careers resulting in a reduction in quality of life. However, tools to measure professional quality of life (ProQoL) are unavailable in Vietnamese. This study aims to develop a Vietnamese version of the ProQoL, and examine ProQoL and its associated factors among doctors and nurses. METHODS: The ProQoL is comprised of 30 items measures compassion satisfaction (CS), burnout (BO), and secondary traumatic stress (STS). The tool was translated into Vietnamese following the Guideline by Guillemin et. al (1993), reviewed by expert panels, and validated for internal consistency and test-retest reliability among 38 health workers working at hospitals in HCMC. The validated tool was then used in a cross-sectional study to measure the ProQoL of full-time doctors and nurses working in clinical departments at the University Medical Center, University of Medicine and Pharmacy at Ho Chi Minh City, Vietnam. In addition to the ProQoL, self-reported data about demographic and occupational characteristics were collected. RESULTS: The Vietnamese version of ProQoL achieved high internal consistency (alphas between 0.85 and 0.91) and Intra-class Correlation Coefficients (ICCs between 0.71 and 0.89) for all subscales. Among 316 health workers, mean scores of CS, BO, STS were 36.4 (SD = 5.4), 24.9 (SD = 5.1), 25.9 (SD = 5.3), respectively, indicating moderate levels of CS, BO and STS. Participants who were older (b = 0.17, 95%CI = 0.08, 0.26), had sufficient perceived income (b = 2.59, 95%CI = 0.93, 4.24), and > 10 years of working experience (b = 2.15, 95%CI = 0.68, 3.62), had higher CS scores. Those who were older (b=-0.15, 95%CI=-0.23, -0.07), had sufficient perceived income (b=-2.64, 95%CI=-4.18, -1.09), > 10 years of experience (b=-1.38, 95%CI=-2.76, -0.01), worked in surgical department (b=-1.46, 95%CI=-2.54, -0.38) and 8 hours/day (b=-1.52, 95%CI=-2.61, -0.44), had lower BO scores. Moreover, those in a relationship (b=-2.27, 95%CI=-3.53, -1.01) and had sufficient perceived income (b=-1.98, 95%CI=-3.64, -0.32) had lower STS scores. CONCLUSIONS: The Vietnamese version of ProQoL is valid and reliable for use among Vietnamese health workers. Age, marital status, perceived income status, years of working experience, daily working hours, and specialty was associated with at least one component of ProQoL but gender, religion, education level, and monthly income were not.

Anti-vaccination attitude trends during the COVID-19 pandemic: A machine learning-based analysis of tweets.

Objective: Vaccine hesitancy has been ranked by the World Health Organization among the top 10 threats to global health. With a surge in misinformation and conspiracy theories against vaccination observed during the COVID-19 pandemic, attitudes toward vaccination may be worsening. This study investigates trends in anti-vaccination attitudes during the COVID-19 pandemic and within the United States, Canada, the United Kingdom, and Australia. Methods: Vaccine-related English tweets published between 1 January 2020 and 27 June 2021 were used. A deep learning model using a dynamic word embedding method, Bidirectional Encoder Representations from Transformers (BERTs), was developed to identify anti-vaccination tweets. The classifier achieved a micro F1 score of 0.92. Time series plots and country maps were used to examine vaccination attitudes globally and within countries. Results: Among 9,352,509 tweets, 232,975 (2.49%) were identified as anti-vaccination tweets. The overall number of vaccine-related tweets increased sharply after the implementation of the first vaccination round since November 2020 (daily average of 6967 before vs. 31,757 tweets after 9/11/2020). The number of anti-vaccination tweets increased after conspiracy theories spread on social media. Percentages of anti-vaccination tweets were 3.45%, 2.74%, 2.46%, and 1.86% for the United States, the United Kingdom, Australia, and Canada, respectively. Conclusions: Strategies and information campaigns targeting vaccination misinformation may need to be specifically designed for regions with the highest anti-vaccination Twitter activity and when new vaccination campaigns are initiated.

Applying Machine Learning to Identify Anti-Vaccination Tweets during the COVID-19 Pandemic.

Anti-vaccination attitudes have been an issue since the development of the first vaccines. The increasing use of social media as a source of health information may contribute to vaccine hesitancy due to anti-vaccination content widely available on social media, including Twitter. Being able to identify anti-vaccination tweets could provide useful information for formulating strategies to reduce anti-vaccination sentiments among different groups. This study aims to evaluate the performance of different natural language processing models to identify anti-vaccination tweets that were published during the COVID-19 pandemic. We compared the performance of the bidirectional encoder representations from transformers (BERT) and the bidirectional long short-term memory networks with pre-trained GLoVe embeddings (Bi-LSTM) with classic machine learning methods including support vector machine (SVM) and naïve Bayes (NB). The results show that performance on the test set of the BERT model was: accuracy = 91.6%, precision = 93.4%, recall = 97.6%, F1 score = 95.5%, and AUC = 84.7%. Bi-LSTM model performance showed: accuracy = 89.8%, precision = 44.0%, recall = 47.2%, F1 score = 45.5%, and AUC = 85.8%. SVM with linear kernel performed at: accuracy = 92.3%, Precision = 19.5%, Recall = 78.6%, F1 score = 31.2%, and AUC = 85.6%. Complement NB demonstrated: accuracy = 88.8%, precision = 23.0%, recall = 32.8%, F1 score = 27.1%, and AUC = 62.7%. In conclusion, the BERT models outperformed the Bi-LSTM, SVM, and NB models in this task. Moreover, the BERT model achieved excellent performance and can be used to identify anti-vaccination tweets in future studies.

Quality of life, depression and correlates in HIV+ people at An Hoa Clinic, Ho Chi Minh City.

This study investigated correlates of quality of life (QOL) among people living with HIV/AIDS (PLWH) at An Hoa Clinic, Ho Chi Minh City, Vietnam. Inclusive criteria were PLWH ≥18 years old, under antiretroviral therapy (ART) for ≥3 months, and consent to participate. PLWH who were illiterate, too ill, or at the final stage of AIDS were excluded. QOL was assessed using WHOQOL-BREF-HIV. Depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale. For every point increased in depression score, QOL decreased 0.13 points in Physical (p < .001), 0.12 points in Psychological and Social Relationships (p < .001), 0.07 points in Level of Independence (p < .001), 0.09 points in Environment (p < .001), and 0.15 points in Personal Beliefs domain (p < .001). PLWH from an economically disadvantaged household had lower QOL scores for all QOL domains but Personal Beliefs with differences ranging from 0.81 points for Social Relationships to 1.77 points for Environment domain. PLWH with a co-morbidity had lower scores whereas those living with a spouse and adhering to ART medication had higher scores in at least one QOL domain. In conclusion, depressive symptoms, household economy, living with a spouse, having a co-morbidity and ART medication adherence were important factors associated with PLWH's QOL.

Changes in depressive symptoms and correlates in HIV+ people at An Hoa Clinic in Ho Chi Minh City, Vietnam.

BACKGROUND: Understanding of depression among Vietnamese people living with HIV (PLWH) is limited. This longitudinal study examines changes in depressive symptoms and identifies its correlates among people living with HIV under antiretroviral therapy at An Hoa Clinic. METHODS: People living with HIV ≥18 years and undergoing antiretroviral therapy for ≥3 months were eligible. Those at final AIDS stage, too ill, or illiterate were excluded due to their inability to complete the self-administered questionnaire. One researcher was present in the clinic for a month inviting PLWH to participate. Data were collected from 242 PLWH at baseline (T1) and 234 after three months (T2). Depressive symptoms was measured by the Center for Epidemiologic Studies Depression Scale (CESD). Social relationship was measured using questions created by World Health Organization. Generalized Estimating Equations were used examining changes in depressive symptoms with CESD cut-off <16/≥16 (mild depression) and cut-off <23/≥23 (major depression). RESULTS: Model 1 (CESD cut-off <16/≥16) showed that participants were not more likely to have depressive symptoms at T2 compared to T1 (OR = 1.15, p > 0.05). Those with a co-morbidity were more likely to have depressive symptoms than those without a co-morbidity (OR = 1.76, p < 0.05). Those with higher social relationship scores were less likely to have depressive symptoms than those with lower scores (OR = 0.76, p < 0.001). Model 2 (CESD cut-off <23/≥23) showed that participants were more likely to have major depressive symptoms at T2 compared to T1 (OR = 1.6, p < 0.01) and those with higher social relationship score were less likely to have major depressive symptoms than those with lower scores (OR = 0.73, p < 0.001). CONCLUSIONS: People living with HIV were not more likely to have depressive symptoms (<16/≥16) but were more likely to have major depressive symptoms (<23/≥23) at T2 vs. T1. Social relationship was found to be strongly associated with depressive symptoms. Associations between age, individual income status, and co-morbidity with depressive symptoms were not decisive. Gender, ethnicity, education, religion, marriage, household economy, and adherence were not correlates.