Recommendations Regarding the Appropriateness of Virtual Care: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To identify and analyze factors influencing the appropriateness of virtual and in-person care and to synthesize current evidence-based recommendations to assist health care providers in determining when virtual or in-person care is most suitable. METHODS: Four databases (MEDLINE, CINAHL, Embase, and APA PsychInfo) and Google Scholar were searched to identify qualitative, quantitative, and mixed methods studies and clinical practice guidelines published between January 2014 and January 2024 focused on appropriateness of virtual care. Articles were extracted and uploaded to Covidence for screening. Two researchers screened the articles independently, and a third researcher resolved any conflicts. Data were extracted from articles, and factors influencing the appropriateness of virtual care were categorized using thematic analysis. RESULTS: The search retrieved 5,136 articles, of which 75 met inclusion criteria and were included in the review. An additional eight articles were identified following a supplemental search of reference lists, resulting in a total of 83 articles included in the study. Six primary concepts influencing the appropriateness of virtual care were identified from the literature (patient characteristics, clinical presentation and disease, timepoint in the care process, burden of care, provider factors, and technology platform) and 22 subconcepts. A flowchart incorporating these concepts was developed to assist in clinical decision-making regarding the suitability of virtual and in-person care. DISCUSSION: Findings from this systematic review provide clinicians with a structured approach to evaluating the suitability of virtual versus in-person care, supporting evidence-based decisions and effective integration of virtual care into the health care system.
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 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 | 0.098 | 0.310 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".