Prediction of adherence to antiretroviral therapy: A one-year longitudinal study
Bibliographic record
Abstract
The aim of this longitudinal study was to identify the determinants of adherence to antiretroviral therapy (ART) in HIV patients over a period of 12 months. A total of 376 individuals living with HIV treated with ART participated in the study. Data were collected at baseline and at three, six, nine and 12 months. Variables assessed were adherence, attitude, outcome expectancies, self-efficacy, patient satisfaction with the relationship with their physician, provision of social support, optimism, CD4 cell count, viral load and side effects. Predictors of adherence in the Generalized Estimated Equation (GEE) were: high perception of self-efficacy (OR=1.68; 95%CI 1.27-2.22), positive attitude towards taking medication (OR=1.56; 95%CI 1.18-2.06), not living alone (OR=1.47; 95%CI 1.04-2.08) and being a male (OR=2.81; 95%CI 1.47-5.34). Subsequent analysis showed that a positive attitude towards taking medication was associated with a high level of patient satisfaction with their physician, high perceived social support, being optimistic, living with HIV for five years or less and experiencing no side effects. Also, a strong sense of self-efficacy was associated with positive perception of social support, high level of patient satisfaction with their physician and not living alone. These results suggest that interventions aimed at improving adherence to ART should focus on reinforcing self-efficacy and developing a positive attitude towards taking medication.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".