Livelihood Security and Adherence to Antiretroviral Therapy in Low and Middle Income Settings: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: We sought to examine the association between livelihood security and adherence to antiretroviral therapy (ARVs) in low- and middle-income countries (LIMC). METHODS: Performing a systematic review, we searched, independently and in duplicate, 7 electronic databases and 2 conference websites for quantitative surveys that examined the association between indicators of livelihood security and adherence to ARVs in LIMC between 2000-2010. Criteria for relevance were applied to complete papers (quantitative study with estimates of associations) and quality assessment was conducted on those deemed relevant. We performed three regressions to measure the association between each type of livelihood and adherence. RESULTS: Twenty original studies and 6 conference abstracts were included, the majority from Africa (n = 16). Seventeen studies and 3 conference abstracts were cross-sectional and 3 studies and 3 abstracts were prospective clinical cohort studies, with considerable variation in quality for studies of each design type. Among the diverse populations represented, we observed considerable variation in associations between measurements of livelihood indicators and increasingly accepted adherence measures, irrespective of study design or quality. A financial capital indicator, financial constraints/payment for ARV medication, was more commonly associated with non-adherence (3/5 studies). A human capital indicator, educational level, was most commonly associated with adherence (11/20 studies). DISCUSSION: Additional better quality research examining livelihood security is required to inform provision of optimal supports for adherence and mitigation of the impacts of HIV/AIDS.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".