Nonface-to-Face Visitation to Restrict Patient Visits for Infection Control: Integrative Review
Bibliographic record
Abstract
BACKGROUND: In the COVID-19 pandemic, a visit restriction policy for patients has been implemented in medical institutions worldwide and visits are being made using alternative communication technologies. This shift has also required the use of platforms to prevent negative consequences of these restrictions. OBJECTIVE: The purpose of this review was to comprehensively explore nonface-to-face visits as an alternative during infection prevention and to synthesize the scientific evidence of their benefits and disadvantages. METHODS: A comprehensive search was conducted via the PubMed, Embase, CINAHL, Cochrane, and Web of Science electronic databases; unpublished trials in the clinical trials register ClinicalTrials.gov; and Virginia Henderson International Nursing Library up to September 10, 2021. The search query was developed according to the guidelines of the Peer Review of Electronic Search Strategies and included keywords on the topics of telemedicine and visitation restrictions. The inclusion criteria were a nonface-to-face modality using telemedicine with family in a hospital setting, experimental and observational studies, and articles written in English. The exclusion criteria were inaccessible in full text, not related to patient or family involvement, mainly focused on the study protocol, or only discussing the pros and cons of telemedicine. RESULTS: Overall, patients' families experienced emotional distress due to restrictions on face-to-face visits. Nonface-to-face virtual visits compensating for these restrictions had a positive effect on reducing the risk of infection to the patient and the family. This further encouraged psychological and physical recovery and decreased psychological distress. However, nonface-to-face virtual technology could not replace the existence of actual families, and technical problems with networks and devices are reported as limitations. CONCLUSIONS: Ensuring the availability of technology and educating on the same in alignment with the characteristics of patients and their families, nonface-to-face virtual visits need to show more potential as an effective patient-centered treatment strategy based on more research and advanced practice.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".