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Record W2095904982 · doi:10.1017/s1049023x00007913

Canadian Operational and Emotional Prehospital Readiness for a Tactical Violence Event

2010· article· en· W2095904982 on OpenAlexaffabout
Daniel Kollek, Michelle Welsford, Karen Wanger

Bibliographic record

VenuePrehospital and Disaster Medicine · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicGun Ownership and Violence Research
Canadian institutionsUniversity of British ColumbiaMcMaster University Medical CentreMcMaster University
Fundersnot available
KeywordsCrewMedical emergencySoftware deploymentPreparednessEmergency medical servicesFirst responderPsychologySuicide preventionEvent (particle physics)Occupational safety and healthPoison controlApplied psychologyMedicineEngineeringAeronauticsPolitical science

Abstract

fetched live from OpenAlex

Providing prehospital care poses unique risks. Paramedics are essentially the only medical personnel who are routinely at the scene of violent episodes, and they are more likely to be assaulted than are other prehospital personnel. In addition to individual acts of violence, emergency medical services (EMS) providers now need to cope with tactical violence, defined as the deployment of extreme violence in a non-random fashion to achieve tactical or strategic goals. This study reviewed two topics; the readiness of EMS crews for violence in their environment and the impact of violence on the EMS crew member. This latter also evaluated the access and effectiveness of emotional support available to caregivers exposed to violent episodes. The results of the survey indicate a significant lack of preparedness for situations involving tactical violence. A total of 89% of respondents either had never had such training or had been trained more than one year ago. Thirty-six percent of respondents had never engaged in a field exercise with other responding agencies, and 4.5% of respondents were not aware of who would be in charge in such an event. In addition, this study indicates that EMS crews are exposed to events with significant emotional impacts without access to appropriate training and adequate support.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.389
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.342
Teacher spread0.324 · 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
Published2010
Admission routes2
Has abstractyes

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