Social–structural factors associated with supportive service use among a cohort of HIV-positive individuals on antiretroviral therapy
Bibliographic record
Abstract
As mortality rates decrease in British Columbia, Canada, supportive services (e.g. housing, food, counseling, addiction treatment) are increasingly conceptualized as critical components of care for people living with HIV/AIDS. Our study investigates social and clinical correlates of supportive service use across differing levels of engagement. Among 915 participants from the Longitudinal Investigations into Supportive and Ancillary health services (LISA) cohort, 742 (81%) reported using supportive services. Participants were nearly twice as likely to engage daily in supportive services if they self-identified as straight (95% confidence interval [CI], adjusted odds ratio [AOR]: 1.69), had not completed high school (95% CI, AOR: 1.97), had an annual income of < $15,000 (95% CI, AOR: 1.81), were unstably housed (95% CI, AOR: 1.89), were currently using illicit drugs (95% CI, AOR: 1.60), or reported poor social capital in terms of perceived neighborhood problems (95% CI, AOR: 1.15) or standard of living (95% CI, AOR: 1.70). Of interest, after adjusting for sociodemographic and socioeconomic variables, no clinical markers remained an independent predictor of use of supportive services. High service use by those demonstrating social and clinical vulnerabilities reaffirms the need for continued expansion of supportive services to facilitate a more equitable distribution of health among persons living with HIV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".