METHOD: A content analysis was conducted on the anonymous posts retrieved from the WSIF platform between 8th March 2020 and 7th July 2022. Around 1457 posts were initially selected for analysis which was reduced to 1006 after removing duplicates and non-relevant posts, such as queries about the addresses of the doctors and other non-mental health-related issues. A thematic analysis of the data was conducted using an inductive approach.
RESULT: The 1006 posts generated four themes and nine sub-themes. All the women mentioned mental health symptoms (n = 1006; 100%). Most also mentioned reasons for seeking mental healthcare (n = 818; 81.31%), healthcare-seeking behavior (n = 667; 66.30%), and barriers to seeking mental healthcare (n = 552; 54.87%). The majority of women described symptoms of stress, depression, and anxiety-like symptoms, which were aggregated under common mental health conditions. Mental health symptoms were ascribed to various external influences, including marital relationship, intrafamilial abuse, and insecurities related to the COVID-19 pandemic. A large proportion of posts were related to women seeking information about mental healthcare services and service providers (psychologists or psychiatrists). The analysis found that most women did not obtain mental healthcare services despite their externalized mental health symptoms. The posts identified clear barriers to women accessing mental health services, including low mental health literacy, the stigma associated with mental healthcare-seeking behavior, and the poor availability of mental health care services.
CONCLUSION: The study revealed that raising mass awareness and designing culturally acceptable evidence-based interventions with multisectoral collaborations are crucial to ensuring better mental healthcare coverage for women in Bangladesh.
MATERIAL AND METHODS: During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.
STATISTICAL ANALYSIS: Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables.
RESULTS: Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed.
CONCLUSIONS: The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.