Impacts of the COVID-19 Pandemic on the Bereaved: A Study of Bereaved Weibo Users

Family grief during the COVID-19 pandemic: a meta-synthesis of qualitative studies

The COVID-19 pandemic has led to a public health crisis, with increases in the number of deaths. As a result, the number of bereaved people has increased significantly. In addition, the measures adopted to control the spread of virus have triggered changes in the subjective and collective bereavement experiences. This systematic literature review aims to summarize and reinterpret the results of qualitative studies on the experience of losing family members during the pandemic by a thematic synthesis. The searches were performed in the Web of Science, Scopus, PubMed/MEDLINE, CINAHL, PsycINFO, and LILACS databases. Among 602 articles identified, 14 were included. Evidence was assessed using the Critical Appraisal Skills Programme tool. Two descriptive themes related to the objective were elaborated in addition to one analytical theme, namely: "Pandemic grief: lonely and unresolved". These themes proved to be interrelated and indicate that experiences of loss in this context were negatively impacted by the imperatives of physical distance, restriction of hospital visits, technology-mediated communication, and prohibition or restriction of funerals. These changes resulted in experiences marked by feelings of loneliness and helplessness, which should be considered when planning intervention strategies that favor communication between family members with the afflicted loved one and with the health care team, enabling welcoming and creating alternatives for farewell rituals. The findings may support further research to test intervention protocols, especially to guide public policies and promote psychological support to bereaved family members after their loss.

Sensing Psychological Well-being Using Social Media Language: Prediction Model Development Study

BACKGROUND

Positive mental health is arguably increasingly important and can be revealed, to some extent, in terms of psychological well-being (PWB). However, PWB is difficult to assess in real time on a large scale. The popularity and proliferation of social media make it possible to sense and monitor online users' PWB in a nonintrusive way, and the objective of this study is to test the effectiveness of using social media language expression as a predictor of PWB.

OBJECTIVE

This study aims to investigate the predictive power of social media corresponding to ground truth well-being data in a psychological way.

METHODS

We recruited 1427 participants. Their well-being was evaluated using 6 dimensions of PWB. Their posts on social media were collected, and 6 psychological lexicons were used to extract linguistic features. A multiobjective prediction model was then built with the extracted linguistic features as input and PWB as the output. Further, the validity of the prediction model was confirmed by evaluating the model's discriminant validity, convergent validity, and criterion validity. The reliability of the model was also confirmed by evaluating the split-half reliability.

RESULTS

The correlation coefficients between the predicted PWB scores of social media users and the actual scores obtained using the linguistic prediction model of this study were between 0.49 and 0.54 (P<.001), which means that the model had good criterion validity. In terms of the model's structural validity, it exhibited excellent convergent validity but less than satisfactory discriminant validity. The results also suggested that our model had good split-half reliability levels for every dimension (ranging from 0.65 to 0.85; P<.001).

CONCLUSIONS

By confirming the availability and stability of the linguistic prediction model, this study verified the predictability of social media corresponding to ground truth well-being data from the perspective of PWB. Our study has positive implications for the use of social media to predict mental health in nonprofessional settings such as self-testing or a large-scale user study.

How social media expression can reveal personality

Background

Personality psychology studies personality and its variation among individuals and is an essential branch of psychology. In recent years, machine learning research related to personality assessment has started to focus on the online environment and showed outstanding performance in personality assessment. However, the aspects of the personality of these prediction models measure remain unclear because few studies focus on the interpretability of personality prediction models. The objective of this study is to develop and validate a machine learning model with domain knowledge introduced to enhance accuracy and improve interpretability.

Methods

Study participants were recruited via an online experiment platform. After excluding unqualified participants and downloading the Weibo posts of eligible participants, we used six psycholinguistic and mental health-related lexicons to extract textual features. Then the predictive personality model was developed using the multi-objective extra trees method based on 3,411 pairs of social media expression and personality trait scores. Subsequently, the prediction model's validity and reliability were evaluated, and each lexicon's feature importance was calculated. Finally, the interpretability of the machine learning model was discussed.

Results

The features from Culture Value Dictionary were found to be the most important predictors. The fivefold cross-validation results regarding the prediction model for personality traits ranged between 0.44 and 0.48 (p < 0.001). The correlation coefficients of five personality traits between the two "split-half" datasets data ranged from 0.84 to 0.88 (p < 0.001). Moreover, the model performed well in terms of contractual validity.

Conclusion

By introducing domain knowledge to the development of a machine learning model, this study not only ensures the reliability and validity of the prediction model but also improves the interpretability of the machine learning method. The study helps explain aspects of personality measured by such prediction models and finds a link between personality and mental health. Our research also has positive implications regarding the combination of machine learning approaches and domain knowledge in the field of psychiatry and its applications to mental health.

The Effect of the COVID-19 Pandemic on Grief Experiences of Bereaved Relatives: An Overview Review

The COVID-19 pandemic has disrupted grief experiences of bereaved relatives and altered accustomed ways of coping with loss. To understand how bereaved relatives experienced grief during COVID-19, a review, using the overview method, was conducted. An overview of empirical data about this subject has been lacking and therefore, PubMed and CINAHL databases were searched for empirical studies published from January 1, 2020 until December 31, 2021. 28 articles were included in the review. Thematic analysis showed different emotional responses, changes in grief, the effect of absence during final moments, a lack of involvement in the caring process, the impact on communities and social support systems and the alteration of funerals among bereaved relatives. During COVID-19, death is characterized by poor bereavement outcomes and health implications, but bereaved also show signs of resilience and coping. Directions for future research about cultural and societal differences in grief and support methods are suggested.