Becoming a "Treatment Success": What Helps and What Hinders Patients from Achieving and Sustaining Undetectable Viral Loads
Bibliographic record
Abstract
Highly active antiretroviral therapy (HAART) adherence research has focused predominantly on individuals with less than optimal clinical outcomes; therefore, little is known about the experiences of individuals who sustain undetectable viral loads. The present study used a qualitative method to explore how individuals who have sustained undetectable viral loads account for their success, and to identify challenges, as well as possible needs, for continued success. Participants were 20 patients at an outpatient infectious disease clinic in an urban center. Participants completed two 60-minute interviews. The Critical Incident Technique was used to identify and classify critical incidents linked with sustaining treatment success. Of the 438 critical incidents collected, 316 were identified as helpful and 122 were identified as unhelpful. Helpful categories included resolving ambivalence, using personal strengths, and fostering helpful relationships. Unhelpful categories were mood, lack of social support, financial difficulties, and medication factors. Doing well on antiretroviral therapy is a dynamic process that requires ongoing attention from both the patient and care provider. The results of this study highlight the efforts of patients to maintain their health and remind care providers not to assume that patients are not facing continuous challenges. Findings from the present study suggest that psychosocial factors do contribute to improved clinical outcomes in patients taking HAART.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| 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".