Material security and adherence to antiretroviral therapy among HIV-positive people who use illicit drugs
Bibliographic record
Abstract
OBJECTIVE: To examine the relationship between poverty, operationalized using a novel material security measure, and adherence to antiretroviral therapy (ART) among people who use illicit drugs (PWUD) in a context of universal access to HIV care. DESIGN: We analyzed data from a community-recruited prospective cohort in Vancouver, Canada (n = 623), from 2014 to 2017. METHODS: We used multivariable generalized mixed-effects analyses to estimate longitudinal factors associated with mean material security score. We then estimated the association between achieving at least 95% adherence to ART and overall mean material score, as well as mean score for three factors derived from a factor analysis. The three-factor structure, employed in the current analyses, were factor 1 (basic needs); factor 2 (housing-related variables) and factor 3 (economic resources). RESULTS: Recent incarceration [β-coefficient (β) = -0.176, 95% confidence interval (95% CI): -0.288 to -0.063], unmet health needs [β = -0.110, 95% CI: -0.178 to -0.042), unmet social service needs (β = -0.264, 95% CI: -0.336 to -0.193) and having access to social services (β= -0.102, 95% CI: -0.1586 to -0.0465) were among the factors associated with lower material security scores. Contrary to expectations that low levels of material security in this population would lead to poor ART adherence, we did not observe a significant relationship between adherence and overall material security score, or for each factor individually. CONCLUSION: Our findings highlight the potentially important role of no-cost, universal access to HIV prevention and treatment, in mitigating the impact of socioeconomic disadvantage on ART adherence.
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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 | 0.004 |
| 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 | 0.000 | 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".