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Record W1977070971 · doi:10.1007/s40037-014-0138-8

Medical students’ perception of dyad practice

2014· article· en· W1977070971 on OpenAlexaff
Martin G. Tolsgaard, Maria Rasmussen, Sebastian Bjørck, Amandus Gustafsson, Charlotte Ringsted

Bibliographic record

VenuePerspectives on Medical Education · 2014
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsThe Wilson CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsDyadPerceptionMedical educationPsychologyThematic analysisClinical PracticeMedicineQualitative researchNursingSocial psychology

Abstract

fetched live from OpenAlex

Training in pairs (dyad practice) has been shown to improve efficiency of clinical skills training compared with single practice but little is known about students' perception of dyad practice. The aim of this study was to explore the reactions and attitudes of medical students who were instructed to work in pairs during clinical skills training. A follow-up pilot survey consisting of four open-ended questions was administered to 24 fourth-year medical students, who completed four hours of dyad practice in managing patient encounters. The responses were analyzed using thematic analysis. The students felt dyad practice improved their self-efficacy through social interaction with peers, provided useful insight through observation, and contributed with shared memory of what to do, when they forgot essential steps of the physical examination of the patient. However, some students were concerned about decreased hands-on practice and many students preferred to continue practising alone after completing the initial training. Dyad practice is well received by students during initial skills training and is associated with several benefits to learning through peer observation, feedback and cognitive support. Whether dyad training is suited for more advanced learners is a subject for future research.

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)0.004
metaresearch head score (Gemma)0.086
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.086
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.010
GPT teacher head0.413
Teacher spread0.403 · 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 designOther design
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

Citations30
Published2014
Admission routes1
Has abstractyes

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