Evaluation study of effect of virtual care education on healthcare providers’ knowledge, confidence, and satisfaction
Bibliographic record
Abstract
Background Virtual care can increase access to healthcare and improve provider efficiency; however, many healthcare providers lack formal education in virtual care delivery, including skills in virtual communication, physical examination adaptations, confidentiality, and billing procedures. This training gap can result in reduced confidence and suboptimal patient care. To address this, an asynchronous continuing professional development (CPD) module was developed. The objective of this study was to evaluate the module’s efficacy regarding satisfaction and changes in knowledge and confidence. Methods The module covered key topics such as virtual visit etiquette, technology troubleshooting, adapted physical examinations, documentation, and remuneration processes. Interactive features included embedded videos, knowledge-check quizzes, and reflective questions. A single-group pre-post quasi-experimental design was used to evaluate its impact. Data were collected via electronic surveys administered at three time points (before, during, and post-module). Surveys included multiple choice questions assessing objective knowledge, and Likert-scale questions assessing confidence levels in virtual care delivery. Open-ended short answer questions captured qualitative feedback. Quantitative data were analyzed using descriptive statistics and paired t-tests or Wilcoxon signed-rank tests where appropriate. Qualitative data were analyzed thematically to identify learner-reported strengths and areas for improvement. Results A total of nine to 22 learners responded at each time point. Respondents were heterogeneous, with most identifying as male (66.7%), general practitioners (55.6%), practicing in hospital settings (55.6%), and in communities of 2,000 to 10,000 people (55.6%). Learners reported high satisfaction with the module’s content relevance, navigation, and interactive components, but requested more interactive components ( e.g ., case-based learning). Statistically significant improvements were observed in confidence levels ( n = 20–21; p < 0.001 to 0.009) and objective knowledge scores ( n = 20–22; p = 0.046). Conclusion This evaluation study demonstrated that the asynchronous virtual care module had a statistically significant impact on objective knowledge and confidence, in addition to having positive satisfaction ratings. Limitations include the small sample size and lack of long-term follow-up to assess sustained practice change. However, these findings support the incorporation of asynchronous, virtual modules into CPD curricula to enhance provider competencies in virtual care delivery. Future directions include integrating additional case-based and specialty-specific content, as well as exploring the module’s scalability for other health professions to promote interprofessional virtual care training.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".