Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

Zhou, Jianlong; Zogan, Hamad; Yang, Shuiqiao; Jameel, Shoaib; Xu, Guandong; Chen, Fang

doi:10.1109/tcss.2020.3047604

Cited by 73 publications

(38 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…60 A multimodal depression model, designed in Australia to detect depression dynamics, showed that individuals became more depressed following the COVID-19 outbreak. 61 Hence, devising a strategy for combating mental health issues during this pandemic should be taken as a prior public health agendum to improve the physical as well as psychological wellbeing of citizens.…”

Section: Discussionmentioning

confidence: 99%

One in Five Street Traditional Coffee Vendors Suffered from Depression During the COVID-19 Pandemic in Harar Town, Ethiopia

Sisay¹,

Gashaw²,

Degefu³

et al. 2021

NDT

View full text Add to dashboard Cite

Background:The COVID-19 crisis has already resulted in an economic and labor market shock and a rise of informal work sectors. Being an informal job, traditional coffee vending can predispose women to a myriad of mental disorders. Therefore, this study was aimed to assess depressive symptoms during the COVID-19 pandemic and associated factors among street traditional coffee vendors in Harar town, Eastern Ethiopia. Methods: A cross-sectional study was employed on 180 women engaged in street traditional coffee vending business in Harar town. Data were collected through a face-to-face interview using a Public Health Questionnaire and analyzed using SPSS version 20. Binary logistic regression was executed to identify factors associated with depression at a cut-off point of P < 0.05. Results:The mean age of the study participants was 31.83 (±10) years. The majority of the participants were currently married (n = 77, 42.8%), attended primary education (n = 68, 37.8%), had a family member of four or more (n = 60, 33.3%), and had two years or less work experience (n = 123, 68.3%). Most of the study participants obtained COVID-19-related information from television (n = 125, 69.4%). The prevalence of depression was found to be 18.9%. Attending primary education (AOR: 0.34; 95% CI: 0.12, 0.98), attending secondary education or higher (AOR: 0.23; 95% CI: 0.07, 0.69), and having four or more family members (AOR: 2.81; 95% CI: 1.14, 6.92) were significantly associated with depression. Conclusion:One in five street traditional coffee vendors suffered from depression during the COVID-19 pandemic. Lower odds of being depressed were observed in those who attended a minimum of primary education. On the contrary, having a greater family size was associated with higher odds of being depressed. This finding will direct the government and other concerned bodies to be involved in the provision of psychological and material support for such informal workers during COVID-19.

show abstract

Section: Discussionmentioning

confidence: 99%

One in Five Street Traditional Coffee Vendors Suffered from Depression During the COVID-19 Pandemic in Harar Town, Ethiopia

Sisay¹,

Gashaw²,

Degefu³

et al. 2021

NDT

View full text Add to dashboard Cite

show abstract

“…Studies conducted throughout the world have consistently found that the lethal spread of COVID-19 and its associated lockdowns have been associated with greater levels of anxiety and depression at the population level [47][48][49][50][51][52][53][54][55][56][57][58][59][60][61]. Several of these studies also noted greater perceived risk of contracting COVID-19 to be significantly associated with depression [53,56,58,59,62].…”

Section: Depressionmentioning

confidence: 97%

Behavioral Correlates of COVID-19 Worry: Stigma, Knowledge, and News Source

Meltzer

Chang

Lieff

et al. 2021

IJERPH

View full text Add to dashboard Cite

Non-adherence to COVID-19 guidelines may be attributable to low levels of worry. This study assessed whether endorsing COVID-19-stigmatizing restrictions, COVID-19 knowledge, and preferred news source were associated with being ‘very worried’ versus ‘not at all’ or ‘somewhat’ worried about contracting COVID-19. Survey data were collected in July–August 2020 from N = 547 New York State (NYS) and N = 504 national Amazon MTurk workers. Respondents who endorsed COVID-19 stigmatizing restrictions (NYS OR 1.96; 95% CI 1.31, 2.92; national OR 1.80; 95% CI 1.06, 3.08) and consumed commercial news (NYS OR 1.89; 95% CI 1.21, 2.96; national OR 1.93; 95% CI 1.24, 3.00) were more likely to be very worried. National respondents who consumed The New York Times (OR 1.52; 95% CI 1.00, 2.29) were more likely to be very worried, while those with little knowledge (OR 0.24; 95% CI 0.13, 0.43) were less likely to be very worried. NYS (OR 2.66; 95% CI 1.77, 4.00) and national (OR 3.17; 95% CI 1.95, 5.16) respondents with probable depression were also more likely to be very worried. These characteristics can help identify those requiring intervention to maximize perceived threat to COVID-19 and encourage uptake of protective behaviors while protecting psychological wellbeing.

show abstract

“…Li et al (2020) gather large scale, pandemicrelated twitter data and infers depression based on emotional characteristics and sentiment analysis of tweets. Zhou et al (2020) focus on detecting community level depression in Australia during the pandemic. They use the distant-supervision methodologies of Shen et al (2017) to gather a balanced dataset, they utilise the methodology of Coppersmith et al (2014) to model the rates of depression and observing the relationship with the number of COVID-19 infections in the community.…”

Section: Mental Health Monitoring During Covid-19 Pandemicmentioning

confidence: 99%

Temporal Mental Health Dynamics on Social Media

Tabak¹,

Purver²

2020

Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020

View full text Add to dashboard Cite

We describe a set of experiments for building a temporal mental health dynamics system. We utilise a pre-existing methodology for distantsupervision of mental health data mining from social media platforms and deploy the system during the global COVID-19 pandemic as a case study. Despite the challenging nature of the task, we produce encouraging results, both explicit to the global pandemic and implicit to a global phenomenon, Christmas Depression, supported by the literature. We propose a methodology for providing insight into temporal mental health dynamics to be utilised for strategic decision-making.

show abstract

Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia

Cited by 73 publications

References 17 publications

One in Five Street Traditional Coffee Vendors Suffered from Depression During the COVID-19 Pandemic in Harar Town, Ethiopia

One in Five Street Traditional Coffee Vendors Suffered from Depression During the COVID-19 Pandemic in Harar Town, Ethiopia

Behavioral Correlates of COVID-19 Worry: Stigma, Knowledge, and News Source

Temporal Mental Health Dynamics on Social Media

Contact Info

Product

Resources

About