Virtual care and COVID-19: A survey study of adoption, satisfaction and continuing education preferences of healthcare providers in Newfoundland and Labrador, Canada
Bibliographic record
Abstract
Introduction: Virtual care has expanded during COVID-19 and enabled continued access to healthcare services. For many healthcare providers, the adoption of virtual care has been a new experience in the provision of healthcare services. The purpose of this survey study was to explore healthcare providers' experiences with virtual care during COVID-19. Methods: A web-based survey-questionnaire was developed by applying Rogers' theory of diffusion of innovation and distributed to healthcare providers (physicians, nurses and allied health professionals) in Newfoundland and Labrador, Canada to explore virtual care experiences, satisfaction and continuing professional development (CPD) needs. Analyses included descriptive statistics and thematic analysis of survey responses. Results: = 432) indicated they were currently offering virtual care and a majority (68.9%) reported it has improved their work experience. Telephone appointments were preferred over videoconferencing by respondents, with key challenges including the inability to conduct a physical exam, patients' cell phone services being unreliable and patients knowing how to use videoconferencing. Majority of respondents (57.5%) reported quality of care by telephone was lower than in-person, whereas quality of care by videoconferencing was equivalent to in-person. Main benefits of virtual care included increased patient access, ability to work from home, and reduction in no-show appointments. Key supports for adopting virtual care included in-house organizational supports (e.g., technical support staff), local colleague support, and technology training. Important topics for virtual care CPD included complying with regulatory standards/rules, understanding privacy or ethical boundaries, and developing competency and digital professionalism while engaging in virtual care. Discussion: Beyond the COVID-19 pandemic, virtual care will have a continuing role in enhancing continuity of care through access that is more convenient. Survey findings reveal a number of opportunities for supporting healthcare providers in use of virtual care, including CPD, guidelines and resources to support adaptation to virtual care provision (e.g., virtual examinations/assessments), as well as patient educational support.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".