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Record W4293246414 · doi:10.1177/08861099221099318

Supporting Newcomer Women Who Experience Intimate Partner Violence and Their Children: Insights From Service Providers

2022· article· en· W4293246414 on OpenAlexafffundabout
Crystal J. Giesbrecht, Daniel Kikulwe, Ailsa M. Watkinson, Christa Sato, David Este, Anahit Falihi

Bibliographic record

VenueAffilia · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of ReginaUniversity of TorontoYork UniversityUniversity of CalgarySaskatchewan Health
FundersPrairieaction Foundation
KeywordsDomestic violenceService providerFocus groupEmpathyCompassionService (business)Qualitative researchPsychologyPatienceNursingPoison controlSuicide preventionMedicineSocial psychologyBusinessSociologyMedical emergencyPolitical science

Abstract

fetched live from OpenAlex

This qualitative study adds to research on the experiences of professionals who support newcomer women who have experienced intimate partner violence (IPV). Findings from seven focus groups with 32 service providers from newcomer-serving and domestic violence agencies in Saskatchewan, Canada, include newcomer survivors’ experiences of isolation, the impact of IPV on newcomer children, and challenges and opportunities for supporting newcomer women who have experienced IPV. Service providers described gaps in existing services and the need for additional services; they also described ways of working effectively with newcomer women survivors of IPV and their children. Professionals indicated the importance of a trauma-and-violence-informed, survivor-centered approach and highlighted the need for compassion, empathy, and patience when working with newcomer women who have experienced IPV. This article includes recommendations for service providers, including IPV shelters and services and newcomer-serving agencies, to improve service to newcomer survivors.

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)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0090.006
Scholarly communication0.0040.004
Open science0.0010.006
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.016
GPT teacher head0.297
Teacher spread0.281 · 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 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

Citations10
Published2022
Admission routes3
Has abstractyes

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