Determinants of adherence to highly active antiretroviral therapy among HIV-1-infected patients in Côte d'Ivoire
Bibliographic record
Abstract
OBJECTIVE: To assess adherence to HAART and to determine factors associated with poor adherence among HIV-1-infected patients in Abidjan, Côte d'Ivoire. METHODS: A prospective observational study of 614 consecutive patients attending an HIV/AIDS outpatient clinic. Adherence was measured twice at 3-month intervals by self-report of missing doses over 4 days. An adherence level of less than 95% was defined as poor adherence. We used generalized estimating equation models for binomial distribution with repeated measures for data analysis. RESULTS: Of the 591 subjects who completed the study, 74.3% reported adherence levels of 95% or greater. Six factors were independently related to poor adherence: age less than 35 years [relative risk (RR) 1.45; 95% confidence interval (CI) 1.17-1.79], absence of social support (RR 1.66; 95% CI 1.24-2.24), number of daily pills 10 or more (RR 1.47; 95% CI 1.14-1.91), time of adherence assessment (first versus second time assessment RR 1.36; 95% CI 1.12-1.66), CD4 cell count of 250 cells/mul or greater (RR 1.43; 95% CI 1.10-1.88), and not being less worried about HIV infection now that treatments have improved (RR 1.26; 95% CI 1.01-1.58). Drug supply interruptions in the pharmacies were reported by 10.0% of the non-adherent patients as the reason for missing pills. CONCLUSION: Psychosocial factors were found to impact adherence and should be analysed in more detail by further studies. Scaling up antiretroviral therapy in sub-Saharan Africa should be preceded by reliable drug supply and distribution systems.
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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 | 0.003 |
| 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 | 0.000 |
| 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".