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Record W4410302205 · doi:10.1371/journal.pdig.0000857

Evaluation of the virtual care experience for persons in prospective cohorts with HIV during the COVID pandemic

2025· article· en· W4410302205 on OpenAlexafffundabout
Sharon Walmsley, Majid Nabipoor, Valérie Martel‐Laferrière, Mona Loutfy, Curtis Cooper, Marie-Louise Vachon, Bryan Boyachuk, Pamela Aldebes, Marina B. Klein

Bibliographic record

VenuePLOS Digital Health · 2025
Typearticle
Languageen
FieldHealth Professions
TopicMobile Health and mHealth Applications
Canadian institutionsUniversité LavalOttawa HospitalCentre Hospitalier de l’Université de MontréalUniversity of TorontoUniversity Health NetworkWomen's College Hospital
FundersMerck Canada
KeywordsMedicinePandemicCohortViral loadCohort studyDepression (economics)Medical prescriptionComorbidityFamily medicineHuman immunodeficiency virus (HIV)GerontologyCoronavirus disease 2019 (COVID-19)PsychiatryInternal medicineDiseaseNursing

Abstract

fetched live from OpenAlex

The COVID pandemic necessitated shifting to virtual care. Our aim was to describe, and identify the challenges and satisfaction with the virtual care experience of a subset of participants from two established Canadian Trials Network (CTN) cohorts: CTN 222 (HIV/HCV coinfection) and CTN 314: CHANGE HIV (Correlates of Healthy Aging in geriatric HIV infection) - persons > 65 years age. We hypothesized that vulnerable populations could face challenges with virtual care related to age, mental health or drug addiction. Consenting participants provided demographic information, completed a non-validated 18-item self- administered questionnaire on their virtual care experience, and reported HIV specific laboratory collection and prescription refills during the COVID pandemic. Data on CD4 T lymphocyte counts and HIV viral loads were extracted from medical records. A total of 454 individuals participated between February 2021 and March 2023, including 133 from CTN 314 and 321 from CTN 222. Overall, 55.3% engaged in virtual care. In multivariable regression models (analysis with SAS and R software) use of virtual care was higher in the aging cohort (p < .0001) but did not vary with current alcohol, drug use or self-reported depression (p > .05). The most common reason for not engaging was that it was failure to offer. Of those who engaged, 55% reporting being very satisfied, 36.3% somewhat satisfied, and 8.8% not satisfied. Ten percent of the older and 16% of the HCV cohort, reported technology difficulties as a barrier to use. Those with a detectable HIV viral load were more likely to engage in virtual care, p < .05. 81.3% of participants had HIV blood tests as frequently as before the COVID-19 pandemic. Despite high satisfaction, the majority (80%) prefers in person visits. When offering virtual care, clinics need to ensure all eligible patients are aware of how to access the services and consider patient needs and preferences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.185
Threshold uncertainty score0.368

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.058
GPT teacher head0.428
Teacher spread0.370 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes3
Has abstractyes

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