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Record W3041429974 · doi:10.1177/0886260520934428

Complexities of the Police Response to Intimate Partner Violence: Police Officers’ Perspectives on the Challenges of Keeping Families Safe

2020· article· en· W3041429974 on OpenAlexafffund
Michael Saxton, Peter G. Jaffe, Myrna Dawson, Anna‐Lee Straatman, Laura Olszowy

Bibliographic record

VenueJournal of Interpersonal Violence · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of GuelphWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDomestic violenceCriminal justiceQualitative researchIntervention (counseling)Poison controlSuicide preventionConsistency (knowledge bases)Workplace violenceHuman factors and ergonomicsOccupational safety and healthPsychologyCriminologyMedicinePublic relationsNursingPolitical scienceSociologyEnvironmental health

Abstract

fetched live from OpenAlex

= 15), the present study examined police perspectives toward their response to intimate partner violence (IPV). Qualitative analyses indicated several challenges police officers face in responding to IPV, including barriers at the systemic, organizational, and individual levels. Police officers in the current study also identified recommendations toward overcoming barriers. Overall, results continue to underscore a lack of police consistency toward addressing IPV, including inconsistent approaches to assessing and managing risk posed to families. Conversely, qualitative results point to several recommendations that heavily involve collaboration between community and justice partners. Implications for future research and practice include further examination of the identified recommendations, a continued focus on developing training that addresses the risk posed to high-risk families, and further development of collaborative approaches toward the prevention and intervention of IPV.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.167
Threshold uncertainty score0.604

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.055
GPT teacher head0.334
Teacher spread0.278 · 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 designQualitative
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

Citations34
Published2020
Admission routes2
Has abstractyes

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