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Record W2039179625 · doi:10.1093/police/pas023

Alcohol Abuse, PTSD, and Officer-Committed Domestic Violence

2012· article· en· W2039179625 on OpenAlexaff
Karen Oehme, Elizabeth Donnelly, Aaron M. Martin

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2012
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsOfficerPsychologyDomestic violenceCriminologyMedical emergencyPsychiatrySuicide preventionMedicinePoison controlPolitical scienceLaw

Abstract

fetched live from OpenAlex

In a unique prevention project in a large US state, researchers explored how alcohol abuse and post-traumatic stress disorder (PTSD) rates influence rates of self-reported domestic violence committed by law enforcement officers. Survey methodology with a cross-sectional design was used, and multiple measures and instruments were analyzed. Because of the novel nature of the online curriculum and resources, there was no comparison group. A strong association—not a cause/effect relationship—was found: officers who had PTSD were four times more likely to report using physical violence, officers who had hazardous drinking were four times more likely to report violence, and dependent drinkers were eight times more likely to report being physically violent with an intimate partner. The findings suggest new opportunities for agency action and have resulted in new recommendations for training and policies to help support healthier law enforcement officers. No previous study has explored the link between PTSD, alcohol use, and domestic violence within this population.

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)0.002
metaresearch head score (Gemma)0.003
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 score0.644
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.047
GPT teacher head0.422
Teacher spread0.375 · 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

Citations27
Published2012
Admission routes1
Has abstractyes

Explore more

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