Pandemic upon Pandemic: Middle-Aged and Older Men Who Have Sex with Men Living with HIV Coping and Thriving during the Peak of COVID-19
Bibliographic record
Abstract
When the COVID-19 pandemic emerged in early 2020, not only did it abruptly impede the progress that was being made toward achieving global targets to end the HIV pandemic, but it also created significant impacts on the physical and mental health of middle-aged and older men who have sex with men living with HIV. Utilizing a qualitative, community-based participatory research approach, we conducted semi-structured, one-on-one interviews with 16 ethnoracially diverse, middle-aged and older men who have sex with men living with HIV residing in Southern Nevada, to examine the different ways the COVID-19 pandemic directly impacted their physical and mental health, and explore how they eventually coped and thrived during the peak of the crisis. Using thematic analysis to analyze our interview data, we identified three prominent themes: (1) challenges to obtaining credible health information, (2) the physical and mental health impacts of the COVID-19-pandemic-imposed social isolation, and (3) digital technologies and online connections for medical and social purposes. In this article, we extensively discuss these themes, the current discourse on these themes in academic literature, and how the perspectives, input, and lived experiences of our participants during the peak of the COVID-19 pandemic could be critical to addressing issues they had already been experiencing prior to the emergence of the pandemic in 2020, and just as importantly, helping us best prepare in stark anticipation of the next potentially devastating pandemic.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".