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Record W3154623839 · doi:10.2196/27877

Best Practices for Integrating Medical Students Into Telehealth Visits

2021· article· en· W3154623839 on OpenAlexvenueno aff
Maria Wamsley, Laeesha N. Cornejo, Irina Kryzhanovskaya, Brian W Lin, Joseph Sullivan, Jordan Yoder, Tali Ziv

Bibliographic record

VenueJMIR Medical Education · 2021
Typearticle
Languageen
FieldMedicine
TopicTelemedicine and Telehealth Implementation
Canadian institutionsnot available
Fundersnot available
KeywordsTelehealthPandemicCoronavirus disease 2019 (COVID-19)MedicineHealth careHealth care deliveryMEDLINEMedical education2019-20 coronavirus outbreakTelemedicineNursingMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

Telehealth has become an increasingly important part of health care delivery, with a dramatic rise in telehealth visits during the COVID-19 pandemic. Telehealth visits will continue to be a part of care delivery after the pandemic subsides, and it is important that medical students receive training in telehealth skills to meet emerging telehealth competencies. This paper describes strategies for successfully integrating medical students into telehealth visits in the ambulatory setting based on existing literature and the extensive experience of the authors teaching and learning in the telehealth environment.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.018
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.859
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.018
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.047
GPT teacher head0.521
Teacher spread0.474 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations28
Published2021
Admission routes1
Has abstractyes

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