Virtual care pathways for people living with <scp>HIV:</scp> A mixed‐methods systematic review
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic prompted an unprecedented surge in virtual services, necessitating a rapid shift to digital healthcare approaches. This review focuses on evaluating the evidence of virtual care (VC) in delivering HIV care, considering the complex nature of HIV and the need for tailored-approaches, especially for marginalized populations. METHODS: A mixed-methods systematic review was performed with searches on five databases, covering studies from January 1946 to May 2022. Inclusion criteria involved two-way virtual consultations between healthcare workers and people living with HIV (PLHIV), with detailed descriptions and outcomes. Qualitative and quantitative studies were included, and the risk of bias was assessed using the Newcastle-Ottawa score and Stenfors' framework. RESULTS: Among 4143 identified records, 26 studies met the criteria, with various models of care described. The majority of studies were observational, and videoconferencing was the primary mode of virtual consultation employed. Quantitative analysis revealed PLHIV generally accept VC, with high attendance rates (87%). Mean acceptability and satisfaction rates were 80% and 85%, respectively, while 87% achieved HIV viral suppression. The setting and models of VC implementation varied, with some introduced in response to COVID-19 while others were as part of trials. CONCLUSIONS: VC for PLHIV is deemed an acceptable and effective approach and is associated with good virological outcomes. Data on other health outcomes is lacking. The review underscores the importance of diverse models of care, patient choice and comprehensive training initiatives for both staff and patients. Establishing a 'gold standard' for VC models is crucial for ensuring appropriate and effective reviews of PLHIV in virtual settings.
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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 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".