It’s all about connection: Determinants of social support and the influence on HIV treatment interruptions among people living with HIV in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Social support has previously been found to be associated with improved health outcomes of individuals managing chronic illnesses, including amongst people living with HIV (PLWH). For women and people who use injection drugs who continue to experience treatment disparities in comparison to other PLWH, social support may have potential in facilitating better treatment engagement and retention. In this analysis, we examined determinants of social support as measured by the Medical Outcomes Study - Social Support Survey (MOS-SSS) scale, and quantified the relationship between MOS-SSS and HIV treatment interruptions (TIs) among PLWH in British Columbia, Canada. METHODS: Between January 2016 and September 2018, we used purposive sampling to enroll PLWH, 19 years of age or older living in British Columbia into the STOP HIV/AIDS Program Evaluation study. Participants completed a baseline survey at enrolment which included the MOS-SSS scale, where higher MOS-SSS scores indicated greater social support. Multivariable linear regression modeled the association between key explanatory variables and MOS-SSS scores, whereas multivariable logistic regression modeled the association between MOS-SSS scores and experiencing TIs while controlling for confounders. RESULTS: Among 644 PLWH, we found that having a history of injection drug use more than 12 months ago but not within the last 12 months, self-identifying as Indigenous, and sexual activity in the last 12 months were positively associated with MOS-SSS, while being single, divorced, or dating (vs. married), experiences of lifetime violence, and diagnosis of a mental health disorder were inversely associated. In a separate multivariable model adjusted for gender, ethnicity, recent homelessness, sexual activity in the last 12 months, and recent injection drug use, we found that higher MOS-SSS scores, indicating more social support, were associated with a lower likelihood of HIV treatment interruptions (adjusted odds ratio: 0.90 per 10-unit increase, 95% confidence interval: 0.83, 0.99). CONCLUSIONS: Social support may be an important protective factor in ensuring HIV treatment continuity among PLWH. Future research should examine effective means to build social support among communities that have potential to promote increased treatment engagement.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".