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Virtual care beyond COVID-19: Patient and physician perspectives.

2022· article· en· W4298139504 on OpenAlexaffabout
Nazek Abdelmutti, Alejandro Berlín, Alyssa Macedo, Jacqueline L. Bender, Zhihui Liu, Janet Papadakos, Mike Lovas, Sheena Melwani, Mary Elliott, Lesley Moody, Iqra Ashfaq, Melanie Powis, Monika K. Krzyzanowska

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsUniversity Health NetworkPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineFamily medicineFeelingLogistic regressionPandemicCoronavirus disease 2019 (COVID-19)Internal medicinePsychology

Abstract

fetched live from OpenAlex

389 Background: The COVID-19 pandemic catalyzed rapid implementation of virtual care (VC), resulting in new opportunities to integrate technology and a need to evaluate patient and provider experiences. To inform sustainment beyond COVID-19, we evaluated perceptions of VC at a comprehensive cancer centre in Toronto, Canada. Methods: Physicians who provided VC during the pandemic, and patients with a valid email address on file and at least one visit with centre in the preceding 12 months were eligible to participate. Survey invitations were disseminated between May and July 2021 via email using a modified Dillman approach. The survey examined the implementation outcomes of acceptability, adoption, and appropriateness. Unadjusted associations between patient demographic variables and preference for in-person visits were evaluated using univariate logistic regression models. Results: 41% (100/246) of physicians and 15% (2,343/15,169) of patients completed the survey. The majority of patients were Caucasian (77%), college or university educated (78%), had solid malignancies (73%), and were in the follow-up phase (47%); 50% were male. The median age was 66 (IQR: 58-74). A greater proportion of patients expressed satisfaction with VC than providers (81% and 53%). Conversely, a greater proportion of providers felt that care delivered virtually was worse than care delivered in-person (45% vs 26%). Interestingly, many patients (69%) and physicians (40%) reported feeling they could maintain a good relationship through VC while at the same time reporting concerns that VC would detract from the human interaction they value as part of care (patients: 60%; providers: 82%). Patients expressed relatively equal preference for phone vs video visits (40% vs 31%), but indicated concerns about wait times for VC visits. The majority of physicians (37%) estimated that 10-29% of their practice would remain virtual post-COVID, however physicians expressed concerns with increased workload (72%), decreased efficiency (40%), and increased worry about missing relevant clinical information (61%). The majority of patients and physicians reported that VC was not appropriate for first consultations and discussions of prognosis, and most appropriate for long-term follow-up. Being born outside of Canada, primary language other than English, lower income, lower functional health literacy, and greater physical mobility were associated with preferring in-person over VC visits. Conclusions: Patients and physicians were satisfied with VC but expressed concerns with the impacts on care quality and experience and highlighted the need for guidelines on appropriate use. Providers expressed greater concerns with VC than patients. More research is needed to formally evaluate the impact of VC on quality performance and clinical outcomes as well as investigate the patient, disease and system factors that are associated with effective virtual cancer care.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.096
GPT teacher head0.502
Teacher spread0.406 · 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 designQualitative
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
Published2022
Admission routes2
Has abstractyes

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