Correlates of social isolation among people living with HIV in British Columbia, Canada
Bibliographic record
Abstract
Our study aims to define and identify correlates of social isolation among people living with HIV (PLHIV). The Longitudinal Investigation into Supportive and Ancillary health services (LISA) study provided a cross-sectional analytic sample of 996 PLHIV in British Columbia, Canada (sampled between 2007 and 2010). Individuals marginalized by socio-structural inequities were oversampled; sampling bias was addressed through inverse probability of participation weighting. Through latent class analysis, three groups were identified: Socially Connected (SC) (n = 364, 37%), Minimally Isolated (MI) (n = 540, 54%) and Socially Isolated (SI) (n = 92, 9%). Correlates of the SI and MI classes, determined through multivariable multinomial regression using the SC class as a reference, include: recent violence (aOR 1.61, 95%CI 1.28–2.02 [MI vs. SC]; aOR 2.04, 95%CI 1.41–2.96 [SI vs. SC]) and a mental health diagnosis (aOR 1.50, 95% CI 1.31–1.72 [MI vs. SC]; aOR 1.43, 95%CI 1.11–1.83 [SI vs. SC]). Women (aOR 0.47; 95%CI 0.32–0.68 [SI vs. SC]), individuals of Indigenous ancestry (aOR 0.59; 95%CI 0.40–0.87 [SI vs. SC]) and people identifying as gay or lesbian (aOR 0.37; 95%CI 0.26–0.52 [SI vs. SC]) were less likely to experience isolation. These findings highlight the importance of supporting communities fostering connectedness and identifies populations susceptible to isolation.
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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 | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| 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".