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Record W4256672160 · doi:10.4324/9781315673073-11

Evidence-based practice principles applied to domestic violence programmes for offenders

2017· book-chapter· en· W4256672160 on OpenAlexaboutno aff
Lynn A. Stewart, Jillian I. Cragg

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
Fundersnot available
KeywordsCriminologyDomestic violencePsychologyPolitical scienceMedical emergencyMedicineHuman factors and ergonomicsPoison control

Abstract

fetched live from OpenAlex

This chapter discusses some principles that provide direction for the design of domestic violence interventions likely to reduce male perpetration of abuse against their female partners. Under the influence of research on evidenced-based practice in the effective corrections literature, correctional programmes have been developed in Canada, the UK, the US, Australia, and New Zealand based on the principles of risk-need-responsivity (RNR) proposed by Andrews and his colleagues. Advocates have critiqued the emphasis on anger management in the treatment of partner violence perpetrators, going so far in many states in the US to prohibit the use of anger management interventions in their treatment guidelines. A key element of responsivity is the development of programme content sensitive to diversity. Culture and its impact in establishing the gender roles of women and men and proscribing appropriate intimate behaviour is perhaps more critical in interventions related to intimate partner violence (IPV) than in any other area of correctional intervention.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.087
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.087
Threshold uncertainty score0.463

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0870.111
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0100.007
Science and technology studies0.0050.018
Scholarly communication0.0120.008
Open science0.0070.008
Research integrity0.0150.014
Insufficient payload (model declined to judge)0.0090.004

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 head0.139
GPT teacher head0.395
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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