Assessing Telemedicine Competencies: Developing and Validating Learner Measures for Simulation-Based Telemedicine Training.
Bibliographic record
Abstract
In 2021, the Association of American Medical Colleges published Telehealth Competencies Across the Learning Continuum, a roadmap for designing telemedicine curricula and evaluating learners. While this document advances educators' shared understanding of telemedicine's core content and performance expectations, it does not include turn-key-ready evaluation instruments. At the University of Oklahoma School of Community Medicine, we developed a year-long telemedicine curriculum for third-year medical and second-year physician assistant students. We used the AAMC framework to create program objectives and instructional simulations. We designed and piloted an assessment rubric for eight AAMC competencies to accompany the simulations. In this monograph, we describe the rubric development, scores for students participating in simulations, and results comparing inter-rater reliability between faculty and standardized patient evaluators. Our preliminary work suggests that our rubric provides a practical method for evaluating learners by faculty during telemedicine simulations. We also identified opportunities for additional reliability and validity testing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.029 | 0.083 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".