Individual Variability in Barriers Affecting People's Decision to Take HAART: A Qualitative Study Identifying Barriers to Being on HAART
Bibliographic record
Abstract
The efficacy of highly active antiretroviral therapy (HAART) has been well documented, particularly among HIV-infected individuals with CD4 cell counts below 200/mm(3). Despite this, eligible individuals may continue to show some reluctance to use HAART. The present study explored the factors influencing patients' decision not to take HAART even though it was medically indicated according to current treatment guidelines and available at no cost to the individual. Fifteen eligible patients at an urban HIV clinic were interviewed regarding the barriers that affected their decision to decline HAART. Most participants had previous experience with antiretroviral therapy and though most believed that HAART was beneficial and felt confident that they could adhere to treatment, they did not feel HAART was the right choice for them at the present time. The Critical Incident Technique was used to identify and classify incidents participants identified as influencing their decision not to take HAART. A total of four categories emerged from the data: Medication Factors, Mood, Lack of Support, and Outcome Expectancies. The results of this study highlight the ambivalence individuals may feel when faced with the prospect of taking HAART. The extent to which each of these factors influenced their decision differed substantially among participants. Suggestions for possible clinical interventions that can be used to address these concerns are offered.
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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 | 0.016 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".