Comparing the care needs of people living with and without HIV in Canadian home and long-term care settings
Bibliographic record
Abstract
BACKGROUND: With the advent of highly active antiretroviral therapy (HAART), HIV has become a manageable chronic infection and individuals with it are living longer. Older individuals with HIV will begin to seek services across the continuum of health care. Whether their care needs differ from those who are HIV negative has not been well-characterized. OBJECTIVES: To compare the demographic characteristics, chronic conditions, presence of infections, and mental health issues among HIV-positive versus HIV-negative individuals in home care, long-term care and complex continuing care settings across Canada. METHODS: This cross-sectional study used interRAI data to compare characteristics of HIV-positive and HIV-negative individuals in long-term care, complex continuing care and home care settings. Chi-square analyses explored differences between groups on co-infections, chronic disease and mental health issues. RESULTS: Data from 1,200,073 people were analyzed of whom 1,608 (0.13%) had HIV. Overall, HIV-positive individuals had more co-infections but fewer chronic diseases than their HIV-negative counterparts. Depression, social isolation and the use of psychotropic medications were generally more prevalent in the HIV-positive cohort. CONCLUSION: People living with HIV make up a small cohort of people with complex needs in home care and institutional settings and their care needs differ from those who are HIV negative. As HIV-positive people age, a better understanding of the context in which these issues are experienced will support appropriate interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".