Smartphones and Mental Health Awareness and Utilization in a Low-Income Urban Community: Focus Group Study
Bibliographic record
Abstract
Background: Mental health disorders pose a significant challenge in low- and middle-income countries (LMICs), contributing substantially to the global disease burden. Despite the high prevalence of these disorders, LMICs allocate less than 1% of health budgets to mental health, resulting in inadequate care and a severe shortage of professionals. Stigma and cultural misconceptions further hinder access to mental health services. These challenges are present in Bangladesh, with high prevalence rates of depression and anxiety, as well as a centralized and underresourced mental health care system. Digital tools, such as smartphone apps and online platforms, offer innovative solutions to these challenges by increasing accessibility, cost-effectiveness, and scalability of mental health interventions. Objective: This study aims to characterize the views around digital tools for mental health among residents of Korail (a major slum in Dhaka, Bangladesh), including the use of smartphones, and investigate acceptable digital tools and barriers and facilitators for digital mental health tools. Methods: A total of 8 focus group discussions were conducted with 38 participants, including individuals with serious mental disorders and their caregivers. The focus group discussions were guided using a semistructured topic guide, which included broad questions on smartphone usage to contextualize digital access, primarily focusing on perceptions of using mobile technology for mental health care. Focus groups were held in Bangla, audio recorded, and transcribed and translated in English. Data were analyzed using thematic analysis in NVivo 14. Results: Participants (mean age 37 y, SD 13.7) were mostly female (30/38, 79%), and 45% (17/38) personally owned smartphones, although 92% (35/38) reported smartphone access within the household. The findings revealed a general lack of awareness and understanding of digital mental health tools among slum residents. However, there was a notable appetite for these tools; participants recognized their potential to provide timely and cost-effective support, reduce hospital visits, and make health care more accessible. Participants highlighted the convenience and communication benefits of smartphones but expressed concerns about misuse such as excessive use, particularly among adolescents. Barriers to the utilization of digital mental health tools included limited technological literacy and accessibility issues. Despite these challenges, participants acknowledged the potential of these tools to bridge the gap in mental health services, especially for those unable to travel. The importance of providing proper guidance and education to maximize the effectiveness of digital tools was emphasized. Conclusions: Digital mental health tools hold promise for improving mental health care in underserved slum communities. This study underscores the need for further research and investment in tailored digital mental health solutions to address the unique needs of slum populations in LMICs.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".