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Record W2011915029 · doi:10.1111/medu.12624

The effect of dyad versus individual simulation‐based ultrasound training on skills transfer

2015· article· en· W2011915029 on OpenAlexaff
Martin G. Tolsgaard, Mette Madsen, Charlotte Ringsted, Birgitte S Oxlund, Anna Oldenburg, Jette Led Sørensen‎, Bent Ottesen, Ann Tabor

Bibliographic record

VenueMedical Education · 2015
Typearticle
Languageen
FieldMedicine
TopicUltrasound in Clinical Applications
Canadian institutionsThe Wilson CentreUniversity of TorontoUniversity Health Network
FundersKøbenhavns Universitet
KeywordsDyadPsychologySimulation trainingTransfer of trainingTransfer (computing)Medical educationComputer scienceMedicineSocial psychologySimulationCognitive psychology

Abstract

fetched live from OpenAlex

CONTEXT: Dyad practice may be as effective as individual practice during clinical skills training, improve students' confidence, and reduce costs of training. However, there is little evidence that dyad training is non-inferior to single-student practice in terms of skills transfer. OBJECTIVES: This study was conducted to compare the effectiveness of simulation-based ultrasound training in pairs (dyad practice) with that of training alone (single-student practice) on skills transfer. METHODS: In a non-inferiority trial, 30 ultrasound novices were randomised to dyad (n = 16) or single-student (n = 14) practice. All participants completed a 2-hour training programme on a transvaginal ultrasound simulator. Participants in the dyad group practised together and took turns as the active practitioner, whereas participants in the single group practised alone. Performance improvements were evaluated through pre-, post- and transfer tests. The transfer test involved the assessment of a transvaginal ultrasound scan by one of two clinicians using the Objective Structured Assessment of Ultrasound Skills (OSAUS). RESULTS: Thirty participants completed the simulation-based training and 24 of these completed the transfer test. Dyad training was found to be non-inferior to single-student training: transfer test OSAUS scores were significantly higher than the pre-specified non-inferiority margin (delta score 7.8%, 95% confidence interval -3.8-19.6%; p = 0.04). More dyad (71.4%) than single (30.0%) trainees achieved OSAUS scores above a pre-established pass/fail level in the transfer test (p = 0.05). There were significant differences in performance scores before and after training in both groups (pre- versus post-test, p < 0.01) with large effect sizes (Cohen's d = 3.85) and no significant interactions between training type and performance (p = 0.59). The dyad group demonstrated higher training efficiency in terms of simulator score per number of attempts compared with the single-student group (p = 0.03). CONCLUSION: Dyad practice improves the efficiency of simulation-based training and is non-inferior to individual practice in terms of skills transfer.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.056
GPT teacher head0.412
Teacher spread0.356 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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

Citations82
Published2015
Admission routes1
Has abstractyes

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