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Record W2954626714 · doi:10.5688/ajpe6531

Simulation-based Crisis Resource Management in Pharmacy Education

2018· article· en· W2954626714 on OpenAlexaffabout
Marie‐Laurence Tremblay

Bibliographic record

VenueAmerican Journal of Pharmaceutical Education · 2018
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPharmacyPharmacy technicianTechnicianMedical educationContext (archaeology)Pharmacy practicePharmacistPsychologyQuality (philosophy)MedicineResource (disambiguation)Pharmacy schoolNursingComputer scienceEngineering

Abstract

fetched live from OpenAlex

<b>Objective.</b> To describe strategies for implementation of simulation-based crisis resource management (CRM) in pharmacy education and present students’ appreciation of an interdisciplinary CRM training at a university in Canada. <b>Methods.</b> In fall 2016, third-year undergraduate pharmacy students at Laval University and pharmacy technician students from Fierbourg school participated in a CRM activity and completed a five-item survey to assess the quality of the CRM activity they had just experienced. Paired t-tests were computed to detect differences of appreciation between pharmacy technician students and pharmacy students. <b>Results.</b> Students rated each item as very good or excellent varying from 81% to 97%. The only difference found between the two types of students was on their overall appreciation of the experience. Pharmacy technician students rated their experience as very good while pharmacy students rated it as excellent. <b>Conclusion.</b> CRM training can easily be adapted to the context of pharmacy education because its key concepts of team management, resource allocation, awareness of environment and dynamic decision-making directly apply to pharmacy practice. Based on the results of this study, students greatly value their CRM training experience. Future research is needed to measure the transfer into practice of CRM principles.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.726
Threshold uncertainty score0.797

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.050
GPT teacher head0.482
Teacher spread0.432 · 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 designOther design
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

Citations16
Published2018
Admission routes2
Has abstractyes

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