Preferences for mHealth Intervention to Address Mental Health Challenges Among Men Who Have Sex With Men in Nepal: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) are disproportionately burdened by poor mental health. Despite the increasing burden, evidence-based interventions for MSM are largely nonexistent in Nepal. OBJECTIVE: This study explored mental health concerns, contributing factors, barriers to mental health care and support, and preferred interventions to improve access to and use of mental health support services among MSM in Nepal. METHODS: We conducted focus groups with MSM in Kathmandu, Nepal, in January 2023. In total, 28 participants took part in 5 focus group sessions. Participants discussed several topics related to the mental health issues they experienced, factors contributing to these issues, and their suggestions for potential interventions to address existing barriers. The discussions were recorded, transcribed, and analyzed using Dedoose (version 9.0.54; SocioCultural Research Consultants, LLC) software for thematic analysis. RESULTS: Participants reported substantial mental health problems, including anxiety, depression, suicidal ideation, and behaviors. Contributing factors included family rejection, isolation, bullying, stigma, discrimination, and fear of HIV and other sexually transmitted infections. Barriers to accessing services included cost, lack of lesbian, gay, bisexual, transgender, intersex, queer, and asexual (LGBTIQA+)-friendly providers, and the stigma associated with mental health and sexuality. Participants suggested a smartphone app with features such as a mental health screening tool, digital consultation, helpline number, directory of LGBTIQA+-friendly providers, mental health resources, and a discussion forum for peer support as potential solutions. Participants emphasized the importance of privacy and confidentiality to ensure mobile apps are safe and accessible. CONCLUSIONS: The findings of this study have potential transferability to other low-resource settings facing similar challenges. Intervention developers can use these findings to design tailored mobile apps to facilitate mental health care delivery and support for MSM and other marginalized groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".