90-90-90-Plus: Maintaining Adherence to Antiretroviral Therapies
Bibliographic record
Abstract
Medication adherence is the "Plus" in the global challenge to have 90% of HIV-infected individuals tested, 90% of those who are HIV positive treated, and 90% of those treated achieve an undetectable viral load. The latter indicates viral suppression, the goal for clinicians treating people living with HIV (PLWH). The comparative importance of different psychosocial scales in predicting the level of antiretroviral adherence, however, has been little studied. Using data from a cross-sectional study of medication adherence with an international convenience sample of 1811 PLWH, we categorized respondent medication adherence as None (0%), Low (1-60%), Moderate (61-94%), and High (95-100%) adherence based on self-report. The survey contained 13 psychosocial scales/indices, all of which were correlated with one another (p < 0.05 or less) and had differing degrees of association with the levels of adherence. Controlling for the influence of race, gender, education, and ability to pay for care, all scales/indices were associated with adherence, with the exception of Berger's perceived stigma scale. Using forward selection stepwise regression, we found that adherence self-efficacy, depression, stressful life events, and perceived stigma were significant predictors of medication adherence. Among the demographic variables entered into the model, nonwhite race was associated with double the odds of being in the None rather than in the High adherence category, suggesting these individuals may require additional support. In addition, asking about self-efficacy, depression, stigma, and stressful life events also will be beneficial in identifying patients requiring greater adherence support. This support is essential to medication adherence, the Plus to 90-90-90.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.023 |
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".