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Record W4292054930 · doi:10.9778/cmajo.20210199

Transitioning to virtual ambulatory care during the COVID-19 pandemic: a qualitative study of faculty and resident physician perspectives

2022· article· en· W4292054930 on OpenAlexafffundvenueabout
Jessica S. S. Ho, Rebecca Leclair, Heather Braund, Jennifer A. Bunn, Ekaterina Kouzmina, Samantha Bruzzese, Sara Awad, Steve Mann, Ramana Appireddy, Boris Zevin

Bibliographic record

VenueCMAJ Open · 2022
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsQueen's University
FundersSoutheastern Ontario Academic Medical OrganizationCanadian Institutes of Health ResearchPhysicians' Services Incorporated Foundation
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)AmbulatoryFamily medicineMedicinePsychologyMedical emergencyMedical educationNursingVirologyInfectious disease (medical specialty)Internal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: The COVID-19 pandemic resulted in a rapid shift from in-person to virtual care delivery for many medical specialties across Canada. The purpose of this study was to explore the lived experiences of resident physicians and faculty related to teaching, learning and assessment during ambulatory virtual care encounters within the competency-based medical education model. METHODS: In this qualitative phenomenological study, we recruited resident physicians (postgraduate year [PGY] 1-5 trainees) and faculty from the Departments of Surgery and Medicine at Queen's University, Ontario, via purposive sampling. Participants were not required to have exposure to virtual care. Interviews were conducted from September 2020 to March 2021 by 1 researcher, and 2 researchers conducted focus groups via Zoom to explore participants' experiences with the transition to virtual care. These were audio-recorded and transcribed verbatim; qualitative data were analyzed thematically. RESULTS: There were 18 male and 19 female participants; 20 were resident physicians and 17 were faculty; 19 were from the Department of Surgery and 18 from the Department of Medicine. All faculty participants had participated in virtual care during ambulatory care; 2 PGY-1 residents in surgery had not actively participated in virtual care, although they had participated in clinics where faculty were using virtual care. The mean age of faculty participants was 38 (standard deviation [SD] 8.6) years, and the mean age of resident physicians was 29 (SD 5.4) years. Overall, 28 interviews and 4 focus groups (range 2-3 participants per group) were conducted, and 4 themes emerged: teaching and learning, assessment, logistical considerations, and suggestions. Barriers to teaching included the lack of direct observations and teaching time, and barriers to assessment included an absence of specific Entrustable Professional Activities (EPAs) and feedback focused on virtual care-related competencies. Logistical challenges included lack of technological infrastructure, insufficient private office space and administrative burdens. Both resident physicians and faculty did not foresee virtual care limiting resident physicians' ability to progress within competency-based medical education. Benefits of virtual care included increased accessibility to patients for follow-up visits, for disclosing patients' results and for out-of-town visits. Suggestions included faculty development, improved access to technology and space, educational guidelines for conducting virtual care encounters, and development of virtual care-specific competencies and EPAs. INTERPRETATION: In the postgraduate program we studied, virtual care imposed substantial barriers on teaching, learning and assessment during the first year of the COVID-19 pandemic. Adapting to new circumstances such as virtual care with suggestions from resident physicians and faculty may help to ensure the continuity of postgraduate medical education throughout the COVID-19 pandemic.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.144
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.118
GPT teacher head0.476
Teacher spread0.358 · 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 teacher head, 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

Citations5
Published2022
Admission routes4
Has abstractyes

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