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Record W6957749829 · doi:10.60692/gdybg-zvj83

The impact of small-group virtual patient simulator training on perceptions of individual learning process and curricular integration: a multicentre cohort study of nursing and medical students

2022· article· en· W6957749829 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languageen
FieldArts and Humanities
TopicMemory, History, Trauma, Identity
Canadian institutionsGeorge Brown College
Fundersnot available
KeywordsTUTORCohortSimulated patientNurse educationPerceptionCohort studyVirtual patientCurriculumProcess (computing)

Abstract

fetched live from OpenAlex

The COVID-19 pandemic has precipitated rapid changes in medical education to protect students and patients from the risk of infection. Virtual Patient Simulators (VPS) provide a simulated clinical environment in which students can interview and examine a patient, order tests and exams, prioritize interventions, and observe response to therapy, all with minimal risk to themselves and their patients. Like high-fidelity simulators (HFS), VPS are a tool to improve curricular integration. Unlike HFS, VPS require limited infrastructure investment and can be used in low-resource settings. Few studies have examined the impact of VPS training on clinical education. This international, multicenter cohort study was designed to assess the impact of small-group VPS training on individual learning process and curricular integration from the perspective of nursing and medical students.We conducted a multi-centre, international cohort study of nursing and medical students. Baseline perceptions of individual learning process and curricular integration were assessed using a 27-item pre-session questionnaire. Students subsequently participated in small-group VPS training sessions lead by a clinical tutor and then completed a 32-item post-session questionnaire, including 25 paired items. Pre- and post-session responses were compared to determine the impact of the small-group VPS experience.Participants included 617 nursing and medical students from 11 institutions in 8 countries. At baseline, nursing students reported greater curricular integration and more clinical and simulation experience than did medical students. After exposure to small-group VPS training, participants reported significant improvements in 5/6 items relating to individual learning process and 7/7 items relating to curricular integration. The impact of the VPS experience was similar amongst nursing and medical students.In this multi-centre study, perceptions of individual learning process and curricular integration improved after exposure to small-group VPS training. Nursing and medical students showed similar impact. Small-group VPS training is an accessible, low-risk educational strategy that can improve student perceptions of individual learning process and curricular integration.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
models agreeAgreement compares identical category sets and study designs across arms.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.039
GPT teacher head0.272
Teacher spread0.233 · 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

Labeled directly by 2 models reading the full record.

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

Citations0
Published2022
Admission routes1
Has abstractyes

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