Reporting guidelines for health care simulation research: Extensions to the CONSORT and STROBE statements
Bibliographic record
Abstract
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, …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.413 | 0.749 |
| Meta-epidemiology (narrow) | 0.008 | 0.008 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.024 | 0.032 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.014 | 0.011 |
| Research integrity | 0.020 | 0.021 |
| Insufficient payload (model declined to judge) | 0.070 | 0.025 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".