Examining the association between stress and antiretroviral therapy adherence among women living with HIV in Toronto, Ontario
Bibliographic record
Abstract
Background We aimed to identify the association between stress and antiretroviral therapy (ART) adherence among women in HIV care in Toronto, Ontario participating in the Ontario HIV Treatment Network Cohort Study (OCS) between 2007 and 2012. Materials and methods We conducted cross-sectional analyses with women on ART completing the AIDS Clinical Trial Group (ACTG) Adherence Questionnaire. Data closest to, or at the last completed interview, were collected from medical charts, through record linkage with Public Health Ontario Laboratories, and from a standardized self-reported questionnaire comprised of socio-demographic and psycho-socio-behavioral measures (Center for Epidemiologic Studies Depression Scale (CES-D), Alcohol Use Disorders Identification Test (AUDIT)), and stress measures (National Population Health Survey). Logistic regression was used to quantify associations with optimal adherence (≥95% adherence defined as missing ≤ one dose of ART in the past 4 weeks). Results Among 307 women, 65.5% had optimal adherence. Women with suboptimal compared to optimal adherence had higher median total stress scores (6.0 [interquartile range (IQR): 3.0–8.1] vs. 4.1 [IQR: 2.0–7.1], p = 0.001), CES-D scores (16 [IQR: 6–28] vs. 12 [IQR: 3–22], p = 0.008) and reports of hazardous and harmful alcohol use (31.1% vs. 17.9%, p = 0.008). In our multivariable model, we found an increased likelihood of optimal adherence with the absence of hazardous and harmful alcohol use (Adjusted Odds Ratio (AOR)=2.20, 95% confidence interval (CI): 1.12–4.32) and a decreased likelihood of optimal adherence with more self-reported stress (AOR = 0.56, 95% CI: 0.33–0.94). Conclusions Interventions supporting optimal ART adherence should address stress and include strategies to reduce or eliminate hazardous and harmful alcohol use for women 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".