scholarly journals The Imminent Mental Health Crisis and the Role Psychologists Can Play to Improve Occupational Health in Times of Coronavirus Pandemic in Pakistan

2020 ◽  
Vol 1 (2) ◽  
pp. 93-97
Author(s):  
Hifza Rabbani ◽  
Yasir Masood
Keyword(s):  
Mental Health ◽  
Large Scale ◽  
The Novel ◽  
Mental Health Crisis ◽  
Health Crisis ◽  
Health Community ◽  
Massive Change ◽  
To Come ◽  
Novel Coronavirus ◽  
Imminent Threat

As expected, the uncertainty of the novel coronavirus has had a major impact on the mental health of the entire population of the world, whether it is in fear of contracting the virus, being stuck at home or being actually infected by it. This much has been proven by research as well but what can mental health practitioners do to combat this imminent threat on a large scale? To name a few, mobilizing their community to bring awareness to the general public and making mental health care easily available for the larger community can be done. The entire mental health community has to come together to bring about a massive change to tackle the crisis of mental health disabilities surging in a post COVID-19 world.

scholarly journals Music for hedonia and eudaimonia during pandemic social isolation

2021 ◽  
Author(s):  
Niels Chr. Hansen
Keyword(s):  
Social Isolation ◽  
Meaning In Life ◽  
Large Scale ◽  
Research Literature ◽  
Music Listening ◽  
The Novel ◽  
Health Crisis ◽  
Musical Engagement ◽  
Contagion Risk ◽  
Novel Coronavirus

The pandemic spread of the novel coronavirus and associated COVID-19 disease in 2020 prompted governments around the world to pursue strict containment protocols to minimize contagion risk. Although restrictions were interpreted more strictly in some countries than in others, widespread social isolation resulted on an unseen scale, leading to severe negative mental health consequences such as loss of hope, increased anxiety, stress, depressive symptoms, and sleep disturbance. During this time, while governments were battling the health crisis, musical engagement provided key, individualized coping strategies for laypeople. This was first demonstrated anecdotally in captivating balcony music videos from Italy and Spain and later substantiated in large-scale, multi-country survey studies. This chapter reviews the emerging research literature on music listening and making during pandemic lockdown to assess whether and how music became a compensatory source of hedonic pleasure versus whether and how it satisfied the need for eudaimonic meaning in life during socially and psychologically impoverished times.

scholarly journals Machine learning for psychological disorder prediction in Indians during COVID-19 nationwide lockdown

2021 ◽  
Vol 15 (1) ◽  
pp. 161-172
Author(s):  
Akshi Kumar
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Predictive Analytics ◽  
Psychological Disorder ◽  
Mental Health Crisis ◽  
Psychological Burden ◽  
Health Crisis ◽  
Disorder Prediction ◽  
Novel Coronavirus ◽  
Mental Health Questionnaire

As the world combats with the outrageous and perilous novel coronavirus, national lockdown has been enforced in most of the countries. It is necessary for public health but on the flip side it is detrimental for people’s mental health. While the psychological repercussions are predictable during the period of COVID-19 lockdown but this enforcement can lead to long-term behavioral changes post lockdown too. Moreover, the detection of psychological effects may take months or years. This mental health crisis situation requires timely, pro-active intervention to cope and persevere the Coro-anxiety (Corona-related). To address this gap, this research firstly studies the psychological burden among Indians using a COVID-19 Mental Health Questionnaire and then does a predictive analytics using machine learning to identify the likelihood of mental health outcomes using learned features of 395 Indian participants. The proposed Psychological Disorder Prediction (PDP) tool uses a multinomial Naïve Bayes classifier to train the model to detect the onset of specific psychological disorder and classify the participants into two pre-defined categories, namely, anxiety disorder and mood disorder. Experimental evaluation reports a classification accuracy of 92.15%. This automation plays a pivotal role in clinical support as it aims to suggest individuals who may need psychological help.

scholarly journals Building a Psychological Ground Truth Dataset with Empathy and Theory-of-Mind During the COVID-19 Pandemic

2021 ◽  
Author(s):  
Yoon Kyung Lee ◽  
Yoonwon Jung ◽  
Inju Lee ◽  
Jae Eun Park ◽  
Sowon Hahn
Keyword(s):  
Mental Health ◽  
Theory Of Mind ◽  
Large Scale ◽  
Mental Health Problems ◽  
Topic Model ◽  
Ground Truth ◽  
Mental Health Crisis ◽  
Emotional Experiences ◽  
Health Crisis ◽  
Preventive Practices

As the mental health crisis deepens with the prolonged COVID-19 pandemic, there is an increasing need for understanding individuals’ emotional experiences. We have built a large-scale Korean text corpus with five self-labeled psychological ground-truths: empathy, loneliness, stress, personality, and emotions. We collected 19,025 documents of daily emotional experiences from 3,805 Korean residents from October to December 2020. We collected 42,128 sentences with different levels of theory-of-mind. Each sentence was annotated by trained psychology students and reviewed by experts. Participants varied in their ages from the early 20s to late 80s and had various social and economic statuses. The pandemic impacted the majority of daily lives, and participants often reported negative emotional experiences. We found the most frequent topics: responses to confirmed cases, health concerns of family members, anger towards people without masks, stress-relief strategies, change of the lifestyle, and preventive practices. We then trained the Word2Vec model to observe specific words that match each topic from the topic model. The current dataset will serve as benchmark data for large-scale and computational methods for identifying mental health levels based on text. This dataset is expected to be used and transformed in many creative ways to mitigate COVID-19-related mental health problems.

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