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Record W7119020680 · doi:10.65264/odzt3394

Article 14: Joint Simulation Training for Correctional Officers and Healthcare Providers in a Correctional System

2022· article· W7119020680 on OpenAlexaboutno aff
Kristin Simard, Dennis Keats, Sharon Reece, Mirette Dubé, Monika Johnson, Alyshah Kaba

Bibliographic record

VenueAdvancing Corrections Journal · 2022
Typearticle
Language
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsTraining (meteorology)Health careExperiential learningJoint (building)Work (physics)Transformative learningVariety (cybernetics)Debriefing

Abstract

fetched live from OpenAlex

In the correctional setting, correctional officers and healthcare providers become immersed in a variety of emergencies in a first responder capacity, requiring organized team approaches and aligned goals. A training gap was identified as healthcare providers and correctional officers facilitate separate training initiatives with minimal focus and education surrounding their collaborative efforts in emergency response situations. The Joint Simulation Training Program (JST) was designed to address this gap, allowing staff the opportunity to practice responding to emergencies together through simulation training. Simulation is an interactive educational technique for teaching knowledge as well as technical, clinical and behavioural skills to participants as they respond to immersive scenarios that replicate real-life events. This is the first joint experiential training initiative undertaken across Provincial Correctional Centres, in Alberta, Canada, and presumably internationally due to a paucity of existing programs in the literature. The case study presented in this paper outlines the design, implementation, and outcomes of this innovative interprofessional training program. The lessons learned from this transformative program should help shape future training initiatives in corrections education, to better prepare these interprofessional teams to work in unpredictable environments.

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)0.003
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.243
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.003
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 head0.061
GPT teacher head0.361
Teacher spread0.300 · 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 designSimulation or modeling
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

Citations0
Published2022
Admission routes1
Has abstractyes

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