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Record W3027755006 · doi:10.1136/bmjstel-2016-000124

Reporting guidelines for health care simulation research: Extensions to the CONSORT and STROBE statements

2016· article· en· W3027755006 on OpenAlexaff
Adam Cheng, David Kessler, Ralph MacKinnon, Todd P. Chang, Vinay Nadkarni, Elizabeth A. Hunt, Jordan Duval‐Arnould, Yiqun Lin, David A. Cook, Martin Pusic, Joshua Hui, David Moher, Matthias Egger, Marc Auerbach

Bibliographic record

VenueBMJ Simulation & Technology Enhanced Learning · 2016
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsOttawa HospitalColumbia CollegeAlberta Children's HospitalUniversity of Calgary
FundersNational Heart, Lung, and Blood InstituteAgency for Healthcare Research and QualityLaerdal Foundation for Acute Medicine
KeywordsHealth careContext (archaeology)Set (abstract data type)Medical educationResearch designPublishingQuality (philosophy)Computer sciencePsychologyMedicineNursingSociology

Abstract

fetched live from OpenAlex

Simulation has seen growing use in health care as a ‘tool, device and/or environment (that) mimics an aspect of clinical care’1 in order to improve health care provider performance, health care processes and, ultimately, patient outcomes.1–5 The use of simulation in health care has been accompanied by an expanding body of simulation-based research (SBR) addressing educational and clinical issues.6–15 Broadly speaking, SBR can be broken down into two categories: (1) research addressing the efficacy of simulation as a training methodology (ie, simulation-based education as the subject of research); and (2) research using simulation as an investigative methodology (ie, simulation as the environment for research).16 ,17 Many features of SBR overlap with traditional clinical or educational research. However, the use of simulation in research introduces a unique set of features that must be considered when designing the methodology, and reported when publishing the study.16–19 As has been shown in other fields of medicine,20 the quality of reporting in health professions education research is inconsistent and sometimes poor.1 ,11 ,21–23 Systematic reviews in medical education have quantitatively documented missing elements in the abstracts and main texts of published reports, with particular deficits in the reporting of study design, definitions of independent and dependent variables, and study limitations.21–23 In research specific to simulation for health care professions education, a systematic review noted many studies failing to ‘clearly describe the context, instructional design or outcomes’.1 Another study found that only 3% of studies incorporating debriefing in simulation education reported all the essential characteristics of debriefing.11 Failure to adequately describe the key elements of a research study impairs the efforts of editors, reviewers and readers to critically appraise strengths and weaknesses24 ,25 or apply and replicate findings.26 As such, …

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)0.413
metaresearch head score (Gemma)0.749
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.587
Threshold uncertainty score0.724

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.4130.749
Meta-epidemiology (narrow)0.0080.008
Meta-epidemiology (broad)0.0220.033
Bibliometrics0.0240.032
Science and technology studies0.0070.010
Scholarly communication0.0190.014
Open science0.0140.011
Research integrity0.0200.021
Insufficient payload (model declined to judge)0.0700.025

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 head0.392
GPT teacher head0.632
Teacher spread0.241 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainReporting
GenreMethods

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

Citations46
Published2016
Admission routes1
Has abstractyes

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