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Record W4289755282 · doi:10.35680/2372-0247.1674

Patient and provider experiences with virtual care during the COVID-19 pandemic: A mixed methods study

2022· article· en· W4289755282 on OpenAlexaffabout
Mars Yixing Zhao, Hisham Elshoni, Jennifer O'Brien, Erin Barbour‐Tuck, Mary E. Walker, Heather Dyck, Andrea Vásquez, Eric Sy, Angela Baerwald, Clara Michaels, Rejina Kamrul, Olivia Reis, Brenda Schuster, Barb Beaurivage, Adam Clay, Mark Lees, Jonathan Gamble

Bibliographic record

VenuePatient Experience Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsSaskatchewan Health AuthoritySaskatchewan HealthUniversity of Saskatchewan
Fundersnot available
KeywordsPandemicThematic analysisMedicineFamily medicineInterquartile rangeCoronavirus disease 2019 (COVID-19)Patient satisfactionDescriptive statisticsQualitative researchNursingInternal medicineDisease

Abstract

fetched live from OpenAlex

The COVID-19 pandemic prompted the rapid uptake of Virtual Care (VC). Positive patient outcomes with VC are previously reported but little is known about the experiences of patients and providers using VC during the pandemic. We aimed to describe patient and primary care provider experiences, satisfaction, perceptions, and attitudes to VC during the COVID-19 pandemic that might explain adoption of VC across the continuum of care and inform sustained uptake. We conducted a sequential explanatory mixed methods study using online surveys and virtual interviews with a convenience sample of primary care providers and patients in a Canadian province (July – December 2020). Eligible participants included patients and primary care providers using VC during the COVID-19 pandemic. Survey responses and interviews were analyzed using descriptive statistics and thematic analysis, respectively. Overall satisfaction was compared using the Mann-Whitney U test. Eighty-five patients and 94 primary care providers responded to the surveys. Patients reported higher overall satisfaction with VC than primary care providers (median [interquartile range]: 4.4 [4.0-4.7] and 3.7 [3.4-3.9] p < 0.001). Ten patients and 11 primary care providers were interviewed. Both groups strongly appreciated VC’s increased access and convenience, identified the lack of compensation as a pre-pandemic barrier to providing VC, and reported willingness to continue VC post-COVID-19 pandemic. The COVID-19 pandemic provided an opportunity for patients and primary care providers to rapidly adopt VC with high satisfaction. Patients and primary care providers viewed VC positively due to its convenience and accessibility; both intend to continue using VC post-pandemic. Experience Framework This article is associated with the Staff & Provider Engagement lens of The Beryl Institute Experience Framework (https://www.theberylinstitute.org/ExperienceFramework). Access other PXJ articles related to this lens. Access other resources related to this lens.

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)0.015
metaresearch head score (Gemma)0.021
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0150.021
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0040.002
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.054
GPT teacher head0.417
Teacher spread0.363 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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