Supporting Web-Based Teaching and Learning of Virtual Care Skills and Competencies: Development of an Evidence-Informed Framework
Bibliographic record
Abstract
BACKGROUND: Professionals across caring disciplines have played a significant role in the development of virtual care, which utilizes remote technologies to offer support and services from a distance. As virtual care becomes increasingly essential, instructors must ensure students are equipped with both interpersonal abilities and digital competencies, merging traditional hands-on methods with online learning. Despite its growing importance, there are a lack of comprehensive frameworks to guide the design and delivery of online learning experiences that foster the development of virtual caring skills and competencies among students in caring professions. OBJECTIVE: To develop an evidence-informed framework to support online teaching and learning of virtual caring skills and competencies. METHODS: We present a synthesis of our research resulting in an evidence-informed framework. We integrated findings from an evidence synthesis, surveys and semi-structured interviews with students and educators, and consultations with key stakeholders. RESULTS: Principles of this framework include: (a) connection and interaction, (b) compassion, empathy, and care, (c) vulnerability, (d) a client-centered focus, (e) inclusivity and accessibility, and (f) flexibility. The framework's four main domains are: (a) virtual caring skills, (b) teaching and learning methods, (c) barriers to teaching, learning, and providing virtual care, and (d) facilitators of teaching learning and providing virtual care. CONCLUSIONS: This framework was developed by and for students and educators to aid in planning, promoting, and enhancing virtual caring skills development. It can be utilized to better equip students to provide virtual care, thereby positively impacting client care and outcomes. This framework can support educators, students, decision-makers, and practice partners to build learning experiences aimed at preparing students to provide virtual care effectively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".