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Record W4408162798 · doi:10.1016/j.ecns.2025.101704

Using exploratory sequential mixed methods design to develop simulation safety practice tool (SSPT)

2025· article· en· W4408162798 on OpenAlex
Mohamed Toufic El Hussein, Giuliana Harvey, Daniel Favell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueClinical Simulation in Nursing · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMount Royal UniversityUniversity of AlbertaAlberta Health Services
FundersInnovationsfonden
KeywordsComputer scienceMedicine

Abstract

fetched live from OpenAlex

Aim To evaluate the face validity of a tool that assesses a student's safety during participation in simulation-based experiences. Design This study is the first phase of an exploratory sequential mixed methods design used for the development of a tool to support undergraduate nursing students' application of safety principles in simulation. Method The authors recruited 10 simulation experts from undergraduate nursing programs in Canada and the United States. Ten semi-structured interviews were conducted to assess the face validity of the tool. Thematic analysis was used to identify and generate themes using Braun and Clarke's approach. Results Based on feedback from the participants, the tool was updated iteratively until the final current version was created. The tool has the potential to support undergraduate nursing students in integrating safety principles in simulation settings; therefore, they may transfer this integration into clinical settings.

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.

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)0.007
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.554
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.325
GPT teacher head0.631
Teacher spread0.306 · 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