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Record W1999535192 · doi:10.1177/0886260514539762

Cost-Effectiveness of Electronic Training in Domestic Violence Risk Assessment

2014· article· en· W1999535192 on OpenAlexaffabout
N. Zoe Hilton, Elke Ham

Bibliographic record

VenueJournal of Interpersonal Violence · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWaypoint Centre for Mental Health CareUniversity of Toronto
Fundersnot available
KeywordsRisk assessmentTraining (meteorology)PopularityPoison controlTest (biology)Human factors and ergonomicsInjury preventionOccupational safety and healthSuicide preventionApplied psychologyMedical educationPsychologyMedicineMedical emergencyComputer scienceComputer securitySocial psychology

Abstract

fetched live from OpenAlex

The need for domestic violence training has increased with the development of evidence-based risk assessment tools, which must be scored correctly for valid application. Emerging research indicates that training in domestic violence risk assessment can increase scoring accuracy, but despite the increasing popularity of electronic training, it is not yet known whether it can be an effective method of risk assessment training. In the present study, 87 assessors from various professions had training in the Ontario Domestic Assault Risk Assessment either face-to-face or using an electronic training program. The two conditions were equally effective, as measured by performance on a post-training skill acquisition test. Completion rates were 100% for face-to-face and 86% for electronic training, an improvement over a previously evaluated manual-only condition. The estimated per-trainee cost of electronic training was one third that of face-to-face training and expected to decrease. More rigorous evaluations of electronic training for risk assessment are recommended.

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)0.007
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.379
Teacher spread0.348 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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