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Record W2317075657 · doi:10.1177/1357633x15575446

A comparison of telemedicine teaching to in-person teaching for the acquisition of an ultrasound skill - a pilot project

2015· article· en· W2317075657 on OpenAlexaff
Anne-Marie Brisson, Peter Steinmetz, Sharon Oleskevich, John Lewis, Andrew Reid

Bibliographic record

VenueJournal of Telemedicine and Telecare · 2015
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsTelemedicineMedical educationPouchMedicineDreyfus model of skill acquisitionHealth careTeaching methodNursingPsychologyMathematics educationSurgery

Abstract

fetched live from OpenAlex

Telemedicine is widely used for medical education but few studies directly investigate how telemedicine teaching compares to conventional in-person teaching. Here we determine whether telemedicine teaching is as effective as in-person teaching for the acquisition of an ultrasound skill important in trauma care. Nurses with no prior ultrasound experience (n = 10) received study material and a teaching session on how to locate and image the hepatorenal space (Morison's pouch). One group of nurses was taught in-person (In-person Group) and the other group was taught via telemedicine (Telemedicine Group). Telemedicine allowed two-way audio and visual communication between the instructor and the nurses. A comparison of the teaching techniques showed that telemedicine teaching was equivalent to in-person teaching for the acquisition of practical and theoretical skills required to locate Morison's pouch. The average time required to locate Morison's pouch after teaching was similar between both groups. The results demonstrate that telemedicine teaching is as effective as in-person teaching for the acquisition of bedside ultrasound skills necessary to identify Morison's pouch. Remote teaching of these bedside ultrasound skills may help in the diagnosis of intra-abdominal bleeding in rural healthcare centers.

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)0.003
metaresearch head score (Gemma)0.007
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.792

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.104
GPT teacher head0.438
Teacher spread0.334 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations22
Published2015
Admission routes1
Has abstractyes

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