Virtual Nursing Intervention Adjunctive to Conventional Care: The Experience of Persons Living With HIV
Bibliographic record
Abstract
BACKGROUND: Persons living with HIV (PLHIV) must adhere optimally to antiretroviral therapy (ART) on a daily basis and for their lifetime to maintain an undetectable viral load, allowing them to preserve their health. Taking advantage of the opportunity that information and communication technologies provide to broaden intervention modalities and intensify clinical follow-up, a virtual nursing intervention consisting of four interactive computer sessions was developed to empower PLHIV to manage their ART and symptoms optimally. Compared with other types of information and communication technologies-assisted interventions such as text messages, HIV Treatment, Virtual Nursing Assistance and Education (VIH-TAVIE) requires a certain degree of active engagement on the part of the user to develop and strengthen the self-management skills to optimize adherence. After the intervention's impact on ART adherence was measured quantitatively, a qualitative study was undertaken to describe how users experience the intervention. Understanding how PLHIV perceive being assisted asynchronously by a virtual nurse was of particular interest. OBJECTIVE: The objective of the study was to explore and describe how PLHIV experience VIH-TAVIE, that is, receiving customized asynchronous accompaniment via a virtual nurse. METHODS: A qualitative study was conducted with 26 PLHIV (20 men, 6 women) who received all four VIH-TAVIE sessions. Participants had been diagnosed with HIV 14 years earlier on average and had been on ART for a mean period of 10 years. The sessions lasted 20-30 minutes each and were received two weeks apart. They are hosted by a virtual nurse who engages the user in a self-management skills-learning process for the purpose of treatment adherence. Semistructured interviews were conducted lasting 30-40 minutes to get participants to share their experience of the intervention through personal stories and what they thought and felt during their participation. Data were analyzed using Miles and Huberman's method, by performing these three steps: (1) data reduction (data coding, summaries); (2) data display (in tables and text form); and (3) recontextualization of results. RESULTS: Content analysis yielded five themes regarding how PLHIV experience VIH-TAVIE: (1) exposure to the virtual nursing intervention; (2) virtual nurse humanizes experience of the computer-delivered intervention; (3) learner's experience of the virtual nursing intervention; (4) perceived benefits following participation in the virtual nursing intervention; and (5) relevance of the virtual nursing intervention in relation to the medication management trajectory. CONCLUSIONS: Analyzing the participants' experience revealed they found the intervention's content and format appropriate. To them, the virtual nurse humanized the experience and helped them acquire new skills for achieving optimal ART adherence. Results seem to underscore the importance of offering the intervention to persons who have more problems with drug intake or who are just beginning ART.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".