Program Development for Enhancing Adherence to Antiretroviral Therapy among Persons Living with HIV
Bibliographic record
Abstract
In this paper the development of a self-management program to optimize long-term adherence to antiretroviral therapy for people living with HIV/AIDS is presented. The program is based on intervention mapping: that is, a framework that facilitates the use of theory and empirical evidence in intervention development. In the preparatory phase we conducted a needs-assessment. The results of this phase were then used in the operational phase in which the program was elaborated as follow: in Step 1 we established program objectives; in Step 2 we translated theoretical methods into practical strategies; and in Step 3 we integrated the strategies into a self-management program which were designed to help individuals mobilize their skills to cope with their antiretroviral therapies (ART). These particular abilities are: ability to integrate ART in daily routine, to cope with side effects, to handle situations in which ART is difficult to take, to interact with health professionals and to maintain relationships with social contacts. To address individuals' resources and skills in conjunction with the experience of taking the medication, we developed two different modalities to deliver the intervention: direct support and virtual support. Direct support consists of four 45-minute individualized, face-to-face sessions with a health professional. The Web application involved at least four interactive sessions with a computer. This application was developed with the intention to support individuals in managing their therapy, in a punctual, real-time mode. Treatment adherence behavior is an indicator or gauge that can reveal problems in being able to manage the therapy.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".