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Record W2325359044

Highly realistic scenario based training simulates the psychophysiology of real world use of force encounters: implications for improved police officer performance

2016· article· en· W2325359044 on OpenAlexafffund
Judith P. Andersen, Marian Pitel, Ashini Weerasinghe, Konstantinos Papazoglou

Bibliographic record

VenueTSpace (University of Toronto) · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsOfficerLaw enforcementStress (linguistics)DutyApplied psychologyTraining (meteorology)PsychologyPsychophysiologyActive dutyMilitary personnelPolitical scienceLawGeography
DOInot available

Abstract

fetched live from OpenAlex

Much police 'Use of Force (UOF)' training focuses on range shooting, classroom-based learning, and minimal exposure to realistic scenarios.Consequently, police officers may not be prepared for real-world critical incidents, due to lack of experience making UOF decisions during high stress.This study compared two SWAT ("Special Weapons and Tactics") teams (n=24) to examine the best-simulated physiological stress responses in real-world law enforcement UOF encounters.Results revealed officer physiological stress to highly realistic scenario training was significantly correlated to the stress responses of active duty police officers.Stress responses during classroom-based scenario trainings were minimal, and not significantly related to stress responses experienced during realistic training scenarios or activity duty emergency calls.

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 score0.598
Threshold uncertainty score0.935

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 head0.092
GPT teacher head0.384
Teacher spread0.292 · 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

Citations63
Published2016
Admission routes2
Has abstractyes

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