Guidelines for Rapport-Building in Telehealth Videoconferencing: Interprofessional e-Delphi Study
Bibliographic record
Abstract
Background: Telehealth training is increasingly incorporated into educational programs for health professions students and practicing clinicians. However, existing competencies and standards primarily address videoconferencing visit logistics, diagnostic modifications, and etiquette, often lacking comprehensive guidance on adapting interpersonal skills to convey empathy, cultural humility, and trust in web-based settings. Objective: This study aimed to establish consensus on the knowledge, skills, and attitudes required for health professions students and clinicians to build rapport with patients in telehealth videoconferencing visits and to identify teaching strategies that best support these educational goals. Methods: An e-Delphi study was conducted using a panel of 12 interprofessional experts in telehealth and telehealth education. Round 1 involved interviews, followed by anonymous surveys in rounds 2-4 to build consensus. Results: All 12 experts participated in rounds 1-3. In total, 19 themes related to rapport-building and 77 specific curriculum items were identified, all achieving the established level of consensus. Conclusions: Using a competency-based education framework, this study provides guidance for health professions educators, teaching clinicians, and students on how to adapt interpersonal skills for telehealth including detailed content related to knowledge, skills, attitudes, and teaching strategies. Future research is needed to test the feasibility, acceptability, and effectiveness of curricula based on these competencies and teaching strategies.
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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 | 0.168 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".