Antiretroviral therapy adherence and retention in care in middle-income and low-income countries: current status of knowledge and research priorities
Bibliographic record
Abstract
PURPOSE OF REVIEW: Adherence to combination antiretroviral therapy (cART) is one of the most important contributing factors to positive clinical outcomes in patients with HIV, and long-term retention of patients in low-income and middle-income countries is emerging as an important issue in rapidly expanding cART programs. This review presents recent developments in both treatment adherence and retention of patients in low-income and middle-income countries. RECENT FINDINGS: Adherence is among the most modifiable variables in treatment, but there still is no 'gold standard' measurement. Best estimates demonstrate that adherence in resource-limited settings is equal or superior to that in resource-rich settings, possibly due to focused efforts on support groups and community acceptance of adherence behaviors. However, long-term data show that sustained efforts to ensure high cART adherence and evidence of intervention effects are critical, but that resource-intensive interventions are not warranted in settings where cART adherence is high. Furthermore, well conducted evaluation of culturally sensitive interventions to maximize pre-cART and post-cART initiation retention is badly needed in low-income and middle-income settings. SUMMARY: Further research is needed to identify risk factors and to improve adherence and retention among children, adolescents, and adults through use of social networks or emerging technologies for patients at risk for poor 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".