Predictors of Antiretroviral Adherence Self-efficacy Among People Living With HIV/AIDS in a Canadian Setting
Bibliographic record
Abstract
BACKGROUND: Suboptimal adherence to antiretroviral therapy (ART) among people living with HIV/AIDS (PLWHA) who use illicit drugs remains an ongoing health concern. Although health outcomes associated with adherence self-efficacy have been well-documented, there is dearth research exploring the predictors of this construct. This study sought to identify possible determinants of adherence self-efficacy among a cohort of PLWHA who use illicit drugs. METHODS: From December 2004 to May 2014, we collected data from the AIDS Care Cohort to evaluate Exposure to Survival Services, a prospective cohort of adult PLWHA who use illicit drugs in Vancouver, Canada. We used multivariate generalized estimating equation analyses to identify longitudinal factors independently associated with higher adherence self-efficacy. RESULTS: Among 742 participants, 493 (66.4%) identified as male and 406 (54.7%) reported white ancestry. In multivariate generalized estimating equation analysis, older age at ART initiation (adjusted odds ratio [AOR] = 1.02, 95% confidence interval [CI]: 1.00 to 1.03) and recent year of baseline interview (AOR = 1.08, 95% CI: 1.05 to 1.11) were independently associated with higher adherence self-efficacy, whereas homelessness (AOR = 0.78, 95% CI: 0.65 to 0.94), ≥daily crack smoking (AOR = 0.81, 95% CI: 0.68 to 0.96), experienced violence (AOR = 0.82, 95% CI: 0.69 to 0.98), and childhood abuse (AOR = 0.75, 95% CI: 0.60 to 0.92) were negatively associated. CONCLUSIONS: These findings highlight the potential role that personal and contextual factors can play in predicting levels of ART adherence self-efficacy. Future research should seek to identify and validate strategies to optimize adherence self-efficacy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".