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Record W1561042586 · doi:10.15537/1658-3175.4791

An introduction to medical simulation

2009· article· en· W1561042586 on OpenAlex
Peter G. Brindley, Yaseen M. Arabi

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.

Bibliographic record

VenueSaudi Medical Journal · 2009
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Alberta Hospital
Fundersnot available
KeywordsPanacea (medicine)SAFERMedicineMultidisciplinary approachPatient safetyDebriefingExperiential learningKey (lock)Multidisciplinary teamMedical educationNursingMedical emergencyEngineering ethicsHealth careAlternative medicine

Abstract

fetched live from OpenAlex

While medical simulation is no panacea, it offers numerous potential strategies for comprehensive and practical training, safer patient care, and for those keen to attract and retain staff. It is a technique, rather than just a technology that promotes experiential and reflective learning. It is also a key strategy to teach Crisis Resource Management skills. Simulation can benefit the individual learner, the multidisciplinary team, and the hospital as a whole. It has been described as a key driver of patient safety, and even as the patient safety laboratory of the future. As such is endorsed by many professional societies in many nations. While challenges remain (and are outlined) there are great opportunities for clinicians, administrators, and educators alike.

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)0.002
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.825
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0140.000

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 head0.024
GPT teacher head0.425
Teacher spread0.401 · 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