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Record W2011211963 · doi:10.1016/j.sbspro.2010.03.256

Internet mediated, peer-to-peer feedback for learning of patient transfer skills: prototype development and testing

2010· article· en· W2011211963 on OpenAlexafffund
Joanne McGregor, Shawn X. Meng, Bill Kapralos, Heather Carnahan, Adam Dubrowski

Bibliographic record

VenueProcedia - Social and Behavioral Sciences · 2010
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsHospital for Sick ChildrenThe Wilson CentreUniversity of Ontario Institute of TechnologyUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaOntario Ministry of Health and Long-Term CareHealth Research Board
KeywordsModerationPeer feedbackThe InternetControl (management)Medical educationPeer groupPsychologyTest (biology)Treatment and control groupsPeer reviewMathematics educationComputer scienceMedicineSocial psychologyWorld Wide WebArtificial intelligence

Abstract

fetched live from OpenAlex

The purpose of this study was to test the educational value of a peer-to-peer feedback system in health professions education. Pre-trained on a patient transfer skills, junior occupational therapy students were randomized into a control group (no additional training), and experimental group (encouraged to participate in peer-to-peer Internet mediated feedback for a week). Results indicate that participants in the experimental group did not outperform those in the control group. Future research will concentrate on adding an expert moderator (the educator) to stimulate the learning process.

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)0.013
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.064
GPT teacher head0.367
Teacher spread0.303 · 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 designBench or experimental
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

Citations1
Published2010
Admission routes2
Has abstractyes

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