Cognitive concerns and uncertainty among people aging with HIV: Implications for gerontology
Bibliographic record
Abstract
Background and Objectives:Cognitive concerns are common among people aging with HIV (PAHIV) and can be a significant source of uncertainty. Age-related uncertainties are heightened by HIV-based stigma, isolation, and concerns about healthcare discrimination. This study explored PAHIV’s experiences and needs PAHIV while navigating cognitive concerns and uncertainty.Research Design and Methods:Purposive sampling recruited 45 PAHIV (≥ 40 years of age) with self-identified cognitive concerns in Ontario and Saskatchewan, Canada, for a demographic survey and 2-hour, peer-led focus group via Zoom. Three coders used content analysis to identify themes across interview transcripts, with input from PAHIV and service providers.Results:Few (n=4) participants were diagnosed with a dementia, yet all individuals endorsed at least five cognitive concerns, with most (n=41) concerned about memory and attention. Across focus groups, PAHIV identified memory, concentration, and mental health issues as salient cognitive concerns. Feelings of uncertainty emerged while navigating cognitive concerns while aging with HIV. Participants discussed factors that negatively or positively impacted interactions with healthcare providers; and articulated unmet needs at the intersection of cognition, aging, and HIV, providing key recommendations for improving support. Discussion and Implications:Aging is an uncertain process; for PAHIV, uncertainty is compounded by HIV and cognitive concerns occurring earlier than in the general population. Participants largely rely on informal support networks, partly due to a lack of formal supports. Tailored psychosocial interventions and resources, improved access to regular cognitive screening at earlier ages, and opportunities to learn about cognitive health are crucial for gerontological HIV care.Keywords: HIV/AIDS, cognition, uncertainty, thematic content analysis, focus groups
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".