MétaCan
Menu
Back to cohort
Record W2928080633 · doi:10.7759/cureus.4366

My Throat is Itchy! An In-situ Simulation for Interprofessional Healthcare Education

2019· article· en· W2928080633 on OpenAlexaffabout
Jennifer Dale-Tam, Kelly Rhodes McBride

Bibliographic record

VenueCureus · 2019
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsOttawa HospitalUniversity of Ottawa Skills and Simulation CentreHeart and Stroke Foundation
Fundersnot available
KeywordsMedicineSession (web analytics)Health careNurse practitionersMedical educationNursingInterprofessional educationTest (biology)Patient safety

Abstract

fetched live from OpenAlex

In-situ simulation occurs in the clinical environment. This allows healthcare providers greater access to the educational session while providing the opportunity to test systems or protocols in place. Anaphylaxis is a rare and life-threatening event. As such, many healthcare providers are uncomfortable managing it. The use of simulation as an educational methodology allows the learners to practice rare, high-risk scenarios in a low-risk environment. There is no negative impact to an actual patient when an in-situ simulation education session is provided. Usually there are positive results due to increased staff awareness and improved process. In the spring of 2015, stakeholders at the outpatient antibiotic therapy program (OPAT) at The Ottawa Hospital (TOH) approached the nurse educator team to develop an education session around anaphylaxis management. The nurse educators chose to design and implement an in-situ simulation scenario involving the inter-professional clinic team. Through the use of inter-professional in-situ simulation the team was able to clarify roles, identify equipment issues and rectify those issues as this technical report describes.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.621

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.055
GPT teacher head0.454
Teacher spread0.399 · 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".

Quick stats

Citations5
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueCureusSame topicSimulation-Based Education in HealthcareFrench-language works237,207