Interventions for Enhancing Adherence to Antiretroviral Therapy (ART): A Systematic Review of High Quality Studies
Bibliographic record
Abstract
We sought to review the effectiveness of interventions designed to improve adherence to antiretroviral therapy (ART) from studies included in a recent Cochrane review that reported a clinical and an adherence outcome, with at least 80% follow-up for 6 months or more. Data were extracted independently and in duplicate, with an adjudicator for disagreements. Risk of bias was assessed using the Cochrane Risk of Bias tool. Of 182 relevant studies in the Cochrane review, 49 were related to ART. Statistical pooling was not warranted due to heterogeneity in interventions, participants, treatments, adherence measures and outcomes. Many studies had high risk of bias in elements of design and outcome ascertainment. Only 10 studies improved both adherence and clinical outcomes. These used the following interventions: adherence counselling (two studies); a once-daily regimen (compared to twice daily); text messaging; web-based cognitive behavioral intervention; face-to-face multi-session intensive behavioral interventions (two studies); contingency management; modified directly observed therapy; and nurse-delivered home visits combined with telephone calls. Patient-related adherence interventions were the most frequently tested. Uniform adherence measures and higher quality studies of younger populations are encouraged.
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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 | 0.018 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".