Barriers and facilitators to virtual care in a geriatric medicine clinic: a semi-structured interview study of patient, caregiver and healthcare provider perspectives
Bibliographic record
Abstract
BACKGROUND: COVID-19-related physical distancing measures necessitated widespread adoption of virtual care (i.e. telephone or videoconference), but patients, caregivers and healthcare providers raised concerns about its implementation and sustainability given barriers faced by older adults. OBJECTIVE: To describe barriers and facilitators experienced by people accessing and providing virtual care in a geriatric medicine clinic. DESIGN: Qualitative semi-structured interview study. SETTING AND PARTICIPANTS: We recruited and interviewed 20 English-speaking patients, caregivers and healthcare providers who participated in virtual care at St. Michael's Hospital's geriatric medicine clinic, Toronto, Canada, between 22 October 2020 and 23 January 2021. METHODS: We analyzed data in two stages: framework analysis and deductive coding to the Theoretical Domains Framework. RESULTS: We included six healthcare providers, seven patients and seven caregivers. We identified eight themes: impact of the COVID-19 pandemic on virtual care uptake, complexity of virtually caring for older adults, uncertain accuracy of virtual assessments, inequity in access to virtual care, importance of caring for the patient-caregiver dyad, assimilating technology into the lives of older adults, impact of technology-related factors on virtual care uptake and impact of clinic processes on integration of virtual care into outpatient care. Further, we identified knowledge, skills, belief in capabilities, and environmental context and resources as key barriers and facilitators to uptake. CONCLUSIONS: Patients, caregivers and healthcare providers believe that there is a role for virtual care after COVID-19-related physical distancing measures relax, but we must tailor implementation of virtual care programs for older adults based on identified barriers and facilitators.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".