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Record W1908443491 · doi:10.36834/cmej.36741

Realism of procedural task trainers in a pediatric emergency medicine procedures course

2015· article· en· W1908443491 on OpenAlexaffvenue
Allan Shefrin, Afshin Khazei, Adam Cheng

Bibliographic record

VenueCanadian Medical Education Journal · 2015
Typearticle
Languageen
FieldMedicine
TopicInnovations in Medical Education
Canadian institutionsUniversity of CalgaryUniversity of British ColumbiaUniversity of Ottawa
Fundersnot available
KeywordsTrainerTask (project management)RealismMedicinePericardiocentesisRating scaleMedical educationComputer scienceSurgeryPsychologyEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Pediatric emergency medicine (PEM) physicians have minimal experience in life saving procedures and have turned to task trainers to learn these skills. Realism of these models is an important consideration that has received little study. METHOD: PEM physicians and trainees participated in a day long procedural training course that utilized commercially available and homemade task trainers to teach pericardiocentesis, chest tube insertion, cricothyroidotomy and central line insertion. Participants rated the realism of the task trainers as part of a post-course survey. RESULTS: The homemade task trainers received variable realism ratings, with 91% of participants rating the pork rib chest tube model as realistic, 82% rating the gelatin pericardiocentesis mold as realistic and 36% rating the ventilator tubing cricothyroidotomy model as realistic. Commercial trainers also received variable ratings, with 45% rating the chest drain and pericardiocentesis simulator as realistic, 74% rating the crichotracheotomy trainer as realistic and 80% rating the central line insertion trainer as realistic. CONCLUSIONS: Task training models utilized in our course received variable realism ratings. When deciding what type of task trainer to use future courses should carefully consider the desired aspect of realism, and how it aligns with the procedural skill, balanced with cost considerations.

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)0.002
metaresearch head score (Gemma)0.067
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.532
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.067
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.021
GPT teacher head0.356
Teacher spread0.335 · 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.

Study designNot applicable
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

Citations8
Published2015
Admission routes2
Has abstractyes

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