Doubts about necessity and concerns about adverse effects: identifying the types of beliefs that are associated with non-adherence to HAART
Bibliographic record
Abstract
This cross-sectional study assessed beliefs about highly active antiretroviral therapy (HAART) and their association with reported adherence to HAART among 109 HIV-positive patients receiving HAART while attending an ambulatory care clinic in Brighton, UK. Patients' beliefs about the necessity for and concerns about HAART and their adherence to it were assessed using validated questionnaires. There was considerable variation in beliefs and reported adherence. A quarter of participants reported low adherence to HAART and this was related to concerns about adverse effects as well as to the way in which each individual balanced concerns against perceptions of necessity. Patients were significantly more likely to report low adherence rates if their concerns were high relative to their perceptions of personal need for HAART. Patients with higher CD4 counts were significantly more likely to hold a view of HAART in which perceptions of necessity were higher relative to their concerns about adverse effects. CD4 was not related to reported adherence. A comparison of most recent viral load results between high and low adherence group identified differences in the predicted direction but these were not statistically significant. These preliminary findings suggest that patients' perceptions of HAART are important determinants of adherence with implications for future research and clinical practice.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".