Visual Analog Scale of ART Adherence
Bibliographic record
Abstract
BACKGROUND: Brief self-reports of antiretroviral therapy adherence that place minimal burden on patients and clinic staff are promising alternatives to more elaborate adherence assessments currently in use. This research assessed the association between self-reported adherence on visual analog scale (VASs) and an existing, more complex self-reported measure of adherence, the AACTG, and the degree to which each method distinguished optimally and suboptimally adherent patients in terms of reported barriers to adherence. METHODS: HIV-infected patients (N = 147) at a southeastern US clinic completed a computerized assessment including an antiretroviral therapy adherence VAS, a modified version of the AACTG, and a measure of adherence. RESULTS: Adherence rates were comparable across the AACTG (81%) and VAS (87%); they significantly correlated (r = 0.585) and produced identical classification of optimal (>90%) or suboptimal (<90%) adherence for 66% of patients. In general, VAS scores tended to be higher than AACTG scores. Suboptimally adherent patients reported more adherence barriers than those classified as optimally adherent, and those so classified by the VAS reported considerably more barriers to adherence than those so classified by the AACTG. CONCLUSIONS: Results generally support the construct validity of the VAS and its use as an easily administered assessment tool that can identify patients with barriers to adherence who might benefit from adherence support interventions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.004 |
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".