Telehealth Competencies: Training Physicians for a New Reality?
Bibliographic record
Abstract
In North America, telehealth increased by 40% between 2019 and 2020 and stabilized at 40% in 2021. As telehealth becomes more common, it is essential to ensure that healthcare providers have the required skills to overcome the challenges and barriers of this new modality of care. While the COVID-19 pandemic has accelerated the design and implementation of telehealth curricula in healthcare education programs, its general adoption is still a major gap and an important barrier to ensuring scaling up and sustainability of the telesshealth practice. Lack of experienced faculty and limited curricular time are two of the most common barriers to expanding telehealth education. Overcoming the barriers of telehealth curricula implementation may require moving away from the classic expert model of learning in which novices learn from experts. As the adoption of telehealth curricula is still in its early stages, institutions may need to plan for faculty development and trainee education at the same time. Questions regarding the timing and content of telehealth education, the interprofessional development of curricula, and the identification of optimal pedagogical methods remain open and crucial. This article reflects on these questions and presents telesimulation as an ideal instructional method for the training of telehealth competencies. Telesimulation can provide opportunities for practical training across a range of telehealth competencies, fostering not only technical proficiency but also communication skills and interprofessional collaboration.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".