Focus on Increasing Treatment Self‐Efficacy to Improve Human Immunodeficiency Virus Treatment Adherence
Bibliographic record
Abstract
PURPOSE: Human immunodeficiency virus (HIV) treatment self-efficacy is the confidence held by an individual in her or his ability to follow treatment recommendations, including specific HIV care such as initiating and adhering to antiretroviral therapy (ART). The purpose of this study was to explore the potential mediating role of treatment adherence self-efficacy in the relationships between Social Cognitive Theory constructs and self- reported ART adherence. DESIGN: Cross-sectional and descriptive. The study was conducted between 2009 and 2011 and included 1,414 participants who lived in the United States or Puerto Rico and were taking antiretroviral medications. METHODS: Social cognitive constructs were tested specifically: behaviors (three adherence measures each consisting of one item about adherence at 3-day and 30-day along with the adherence rating scale), cognitive or personal factors (the Center for Epidemiology Studies Depression Scale to assess for depressive symptoms, the 12-Item Short Form Health Survey (SF-12) to assess physical functioning, one item about physical condition, one item about comorbidity), environmental influences (the Social Capital Scale, one item about social support), and treatment self-efficacy (HIV Adherence Self-Efficacy Scale). Analysis included descriptive statistics and regression. RESULTS: The average participant was 47 years old, male, and a racial or ethnic minority, had an education of high school or less, had barely adequate or totally inadequate income, did not work, had health insurance, and was living with HIV/acquired immunodeficiency syndrome for 15 years. The model provided support for adherence self-efficacy as a robust predictor of ART adherence behavior, serving a partial mediating role between environmental influences and cognitive or personal factors. CONCLUSIONS: Although other factors such as depressive symptoms and lack of social capital impact adherence to ART, nurses can focus on increasing treatment self-efficacy through diverse interactional strategies using principles of adult learning and strategies to improve health literacy. CLINICAL RELEVANCE: Adherence to ART reduces the viral load thereby decreasing morbidity and mortality and risk of transmission to uninfected persons. Nurses need to use a variety of strategies to increase treatment self-efficacy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".