Sexual and mental health of Singaporean gay, bisexual and other men who have sex with men in times of COVID-19: a qualitative study
Bibliographic record
Abstract
OBJECTIVES: COVID-19 had significant influences on everyone's lives. This study aimed to explore impacts of COVID-19 on mental and sexual health and access to health services among gay, bisexual and other men who have sex with men (GBMSM) in Singapore. METHODS: This qualitative study recruited 16 self-identified GBMSM via purposive sampling and semi-structured individual interviews were conducted. Three themes and seven sub-themes were derived from analysis done using the framework method. RESULTS: Participants shared how COVID-19 led to negative emotions and experiences at an intrapersonal level and interpersonal level (with families or partners), which were also worsened by prevailing stigma that GBMSM already face in Singapore and within their social networks. Sexual behaviours associated with HIV and other sexually transmitted infections risk and substance use were seen to be maladaptive coping methods of social isolation due to COVID-19. These dynamics were all exacerbated by the closure of "non-essential" services, which included many important services for mental and sexual health that were relevant to the GBMSM community. CONCLUSIONS: Changes in policies and community efforts should be explored to improve these areas, enhancing the psychosocial and sexual well-being of GBMSM.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".