An exploratory study of factors associated with difficulties in accessing HIV services during the COVID-19 pandemic among Chinese gay and bisexual men in Hong Kong
Bibliographic record
Abstract
BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic has resulted in the disruption of provision of human immunodeficiency virus (HIV) services. This study examined the factors associated with difficulties in accessing HIV services during the COVID-19 pandemic. METHODS: An online survey of 236 Chinese-speaking gay and bisexual men in Hong Kong conducted in 2020. RESULTS: Among those who expressed a need to access HIV services during the COVID-19 pandemic, 22.9%, 33.9% and 43.2% indicated moderate-to-high, mild and no difficulties in accessing these services, respectively. Difficulties in accessing HIV services were positively related to concerns about potential COVID-19 infection, experience of actual impact on health because of COVID-19, disruption in work/studies, and reduced connection to the LGBT+ community during the pandemic. It was also found that difficulties in accessing HIV services were positively associated with frequency of having sex with casual partners, but were not significantly associated with frequency of having sex with regular partners. CONCLUSIONS: This study provides novel empirical evidence for understanding difficulties in accessing HIV services during the COVID-19 pandemic. It found that disruption in work/studies and frequency of having sex with casual partners were associated with difficulties in accessing HIV services.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".