Exploring the Perspectives of Older Adults Living With HIV on Virtual Care: Qualitative Study
Bibliographic record
Abstract
Background: As the population of individuals with HIV ages rapidly due to advancements in antiretroviral therapy, virtual care has become an increasingly vital component in managing their complex health needs. However, little is known about perceptions of virtual care among older adults living with HIV. Objective: This study aimed to understand the perceptions of older adults living with HIV regarding virtual care. Methods: Using an interpretive, qualitative, descriptive methodology, semistructured interviews were conducted with 14 diverse older adults living with HIV. The participants lived in Ontario, Canada, self-identified as HIV-positive, and were aged 50 years or older. Efforts were made to recruit individuals with varying experience with virtual health care. Reflexive thematic analysis was conducted with the interview transcripts to identify prevalent themes. Results: The identified themes included (1) the importance of relationships in virtual care for older adults living with HIV; (2) privacy and confidentiality in virtual care; and (3) challenges and solutions related to access and technological barriers in virtual care. These themes highlight the perceptions of diverse older adults living with HIV concerning virtual care, emphasizing the fundamental role of trust, privacy, and technology access. Conclusions: By embracing the unique perspectives and experiences of this population, we can work toward building more inclusive and responsive health care systems that meet the needs of all individuals, regardless of age, HIV status, or other intersecting identities.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".