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Record W4416202685 · doi:10.4103/cjrm.cjrm_70_24

Can implementation of in situ simulation support rural emergency provider self-confidence and improve patient safety? A mixed-methods study

2025· article· en· W4416202685 on OpenAlexaffvenueabout
Sebastian Diebel, Aidan Wharton, Michelle W. Parker

Bibliographic record

VenueCanadian Journal of Rural Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsNOSM University
Fundersnot available
KeywordsQuality managementHealth carePatient satisfactionTelemedicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Rural emergency departments across Canada face challenges such as limited access to continuing medical education and resource constraints. Our study evaluates in situ Simulation (ISS) as an educational tool in a small rural emergency department, focusing on participant satisfaction, clinician confidence and the identification of latent safety threats (LSTs). METHODS: Five monthly ISS sessions were conducted during active clinical hours, involving physicians, nurses and nurse practitioners selected through convenience sampling. After each session, participants anonymously completed the validated student satisfaction and self-confidence in learning survey and short-answer questions to identify LSTs. RESULTS: Participants reported high satisfaction with ISS, and high confidence in clinical skills post-simulation. Thematic analysis of short-answer responses identified several LSTs in clinical care systems, which were brought for review by department leadership to improve patient and provider safety. CONCLUSION: ISS is a feasible and valuable educational strategy for rural healthcare providers, promoting participant satisfaction and enhancing confidence in managing acute situations. In addition, it effectively identifies safety issues, contributing to improved patient care. This model can inform similar initiatives in other rural settings facing educational and resource challenges. INTRODUCTION: Les services d'urgence en milieu rural à travers le Canada font face à des défis tels que l'accès limité à la formation médicale continue et les contraintes de ressources. Notre étude évalue la simulation in situ (SIS) comme outil pédagogique dans un petit service d'urgence rural, en mettant l'accent sur la satisfaction des participants, la confiance des cliniciens et l'identification des menaces latentes à la sécurité. MTHODES: Cinq séances mensuelles de SIS ont été réalisées pendant les heures cliniques actives, réunissant des médecins, des infirmières et des infirmières praticiennes sélectionnés par échantillonnage de convenance. Après chaque séance, les participants ont rempli de façon anonyme le questionnaire validé Student Satisfaction and Self-Confidence in Learning Survey (SCLS), ainsi que des questions à réponses courtes visant à identifier les menaces latentes à la sécurité. RSULTATS: Les participants ont rapporté un haut niveau de satisfaction à l'égard de la SIS et une grande confiance dans leurs compétences cliniques après les simulations. L'analyse thématique des réponses courtes a permis d'identifier plusieurs menaces latentes à la sécurité dans les systèmes de soins cliniques, lesquelles ont été soumises à l'examen de la direction du service afin d'améliorer la sécurité des patients et des prestataires. CONCLUSION: La SIS constitue une stratégie pédagogique réalisable et précieuse pour les professionnels de la santé en milieu rural, favorisant la satisfaction des participants et renforçant leur confiance dans la gestion des situations aiguës. De plus, elle permet de cerner efficacement des enjeux de sécurité, contribuant ainsi à l'amélioration des soins aux patients. Ce modèle peut inspirer des initiatives similaires dans d'autres contextes ruraux confrontés à des défis éducatifs et de ressources.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.144
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.016
GPT teacher head0.406
Teacher spread0.391 · 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 teacher head, not a consensus.

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".

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Citations1
Published2025
Admission routes3
Has abstractyes

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