Best Practice Guide for Reducing Barriers to Video Call–Based Telehealth: Modified Delphi Study Among Health Care Professionals
Bibliographic record
Abstract
BACKGROUND: Telehealth has grown, especially during the COVID-19 pandemic, improving access for those in remote or underserved areas. However, its implementation faces technological, practical, and interpersonal barriers. OBJECTIVE: The aim of this study was to identify and consolidate best practices for telehealth delivery, specifically for video call sessions, by synthesizing the insights of health care professionals across various disciplines. METHODS: We first identified 15 common telehealth barriers from a preceding scoping review. Subsequently, a modified Delphi method was used, involving 9 health care professionals (physiotherapists, speech and language therapists, dietitians, and midwife) with telehealth experience in qualitative interviews and 2 iterative rounds of web-based surveys to form consensus. RESULTS: This study addressed 15 telehealth barriers and identified 105 best practices. Among these, 20 are technology-related and 85 concern health care practices. Emphasis was placed on setting up telehealth environments, ensuring safety, building relationships and trust, using nonmanual methods, and enhancing observation and assessment skills. Best practice recommendations for dealing with patients or caregiver skepticism or lack of telehealth-specific knowledge were developed. Further, approaches for unstable networks and privacy and IT security issues were identified. Areas with fewer best practices were the lack of technology skills or technology access, unreliability of hardware and software, increased workload, and a lack of caregiver support. CONCLUSIONS: This guide of best practices serves as an actionable resource for health care providers to navigate the complexities of telehealth. Despite a small participant sample and the potential for profession-specific biases, the findings provide a foundation for improving telehealth services and inform future research for its application and education.
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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 | 0.094 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".