Acceptability and feasibility of a virtual intervention to help people living with HIV manage their daily therapies
Bibliographic record
Abstract
We conducted a study of the acceptability and feasibility of a web application which was designed to empower people living with HIV to manage their daily antiretroviral therapies. The application (VIH-TAVIE) consists of four interactive computer sessions with a virtual nurse who guides the user through a learning process aimed at enhancing treatment management capacities. The information furnished and the strategies proposed by the nurse are tailored, based on the responses provided by the user. The application was evaluated in a hospital setting as an adjunct to usual care. The participants (n = 71) had a mean age of 47 years (SD = 7.6). There were 59 men and 12 women. They had been diagnosed with HIV some 15 years earlier and had been on antiretroviral medication for a mean duration of 11 years. Data were collected by acceptability questionnaires, field notes and observations. Most participants found the application easy to use. They learned tips for taking their medication, diminishing adverse side-effects and maintaining a positive attitude towards treatment. Many participants deemed their experience with the application highly satisfactory and felt that it met their needs with respect to strategies and proficiencies despite their long experience of medication use. The results of the study support the feasibility and acceptability of the intervention.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".