Telemedicine and medical education: a mixed methods systematic review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to synthesize and appraise the available research on educational strategies required to prepare medical learners for engaging in telemedicine and virtual care. INTRODUCTION: The COVID-19 pandemic has resulted in significant uptake of virtual care and telemedicine, highlighting the growing need for health care organizations and medical institutions to support physicians and learners navigating this new model of health care delivery, clinical learning, and assessment. Developing a better understanding of how best to prepare medical trainees across the continuum of undergraduate, postgraduate, and continuing professional development to engage in virtual care is critical in ensuring our continued ability to meet educational mandates and provide ambulatory care that is safe, efficient, and timely. INCLUSION CRITERIA: Eligible studies will include medical learners who receive education on how to deliver telemedicine. The quantitative component of the review will compare learners exposed to educational interventions with learners not exposed to an intervention, or to a different intervention. Outcomes will include competencies in telemedicine delivery, knowledge, and behaviors. The qualitative component of the review will explore learners' experiences with the delivery of educational strategies that address telemedicine. METHODS: Embase, MEDLINE, Evidence-Based Medicine Reviews: Cochrane Central Register of Controlled Trials, Web of Science Core Collection, Education Source, and ProQuest Dissertations and Theses Global will be searched to identify published and unpublished studies. No date or language restrictions will be applied. This systematic review will be conducted in accordance with the JBI methodology for mixed methods systematic reviews using a convergent segregated approach. Titles and abstracts of potential studies will be screened, and potentially relevant studies will undergo full-text review for eligibility and critical appraisal of the study methodology. Data will be extracted from those studies selected for inclusion. Findings will be described relating to the effectiveness of educational curricula, initiatives, and best practices in trainee engagement in telemedicine and virtual care. SYSTEMATIC REVIEW REGISTRATION NUMBER: PROSPERO CRD42021264332.
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 | 0.121 | 0.089 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.070 | 0.011 |
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".