Training for virtual care: What do the experts think?
Bibliographic record
Abstract
Introduction: Virtual care has expanded during COVID-19 and enabled continued access to healthcare services. As with the introduction of any new technology in healthcare delivery, the preparation of healthcare providers for adopting and using such systems is imperative. The purpose of this qualitative study was to explore experts' ascribed opinions on healthcare providers' continuing professional development (CPD) needs in virtual care. Methods: Semistructured interviews were conducted with a purposive sample of key informants representing Canadian provincial and national organizations with expertise in virtual care delivery. Results: Three main areas of knowledge, skills, and abilities that would be most helpful for healthcare providers in preparing to adopt and use virtual care were identified. The use of technology necessitates knowledge of how to integrate technology and virtual care in the practice workflow. This includes knowing how to use the technology and the privacy and security of the technology. Providers need to be able to adapt their clinical skills to virtual care and build rapport through good communication with patients. Virtual care is not appropriate for all visits, therefore providers need to understand when an in-person visit is necessary with respect to the nature of the appointment, as well as contextual factors for individual patients. Finally, providers need to adapt their examination skills to 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. Key informants identified barriers and challenges in adopting and using virtual care effectively, fundamental knowledge, skills and/or abilities required, and important topics and/or educational experiences to guide CPD program development on virtual care for healthcare providers.
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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 | 0.020 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".