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Record W2095633536 · doi:10.1177/1524838014557285

Co-victims of Homicide

2014· review· en· W2095633536 on OpenAlexaff
Jennifer Connolly, Ronit Gordon

Bibliographic record

VenueTrauma Violence & Abuse · 2014
Typereview
Languageen
FieldPsychology
TopicGrief, Bereavement, and Mental Health
Canadian institutionsYork University
Fundersnot available
KeywordsHomicideGriefPsychological interventionPsychologyCoping (psychology)Suicide preventionPoison controlOccupational safety and healthCriminologyMedicineClinical psychologyPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

This systematic literature review examines the effects of homicide on surviving family members, the "co-victims" or "survivors" of homicide. A content analysis was conducted on 40 articles identified through a search of the literature. The research samples were predominately located in the United States, but included two U.K. samples, one Jamaican sample, and one sample from Israel. All articles were written in English. Three themes were identified. Nineteen articles explored the psychological, academic, social, occupational, and familial effects of homicide. Thirteen articles considered survivors' grieving process and how it was altered by experiences with the criminal justice system as well as coping strategies used by survivors to deal with their grief. Eight articles explored treatment interventions available to help surviving family members deal with their grief. Key findings from the articles, limitations of the current research, and implications for future research, policy, and practice are included.

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)0.003
metaresearch head score (Gemma)0.021
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.021
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0080.007
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.067
GPT teacher head0.412
Teacher spread0.344 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations84
Published2014
Admission routes1
Has abstractyes

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