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Record W4282968125 · doi:10.1111/fcre.12653

In search of interdisciplinary, holistic and culturally informed services: The case of racialized immigrant women experiencing domestic violence in Ontario

2022· article· en· W4282968125 on OpenAlexaffabout
Purnima George, Archana Medhekar, Ferzana Chaze, Bethany Osborne, Mirelli van Heer, Hafsa Alavi

Bibliographic record

VenueFamily Court Review · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsSheridan CollegeHome and Community Care Support ServicesToronto Metropolitan University
Fundersnot available
KeywordsImmigrationDomestic violencePsychological interventionPoliticsGender studiesFace (sociological concept)SociologyCriminologyPolitical scienceMedicineSuicide preventionPoison controlNursingLawSocial scienceMedical emergency

Abstract

fetched live from OpenAlex

Abstract Domestic Violence (DV) is a social issue concerning all women, but not all women are oppressed or impacted in the same way. This paper draws on an analysis of fifteen closed Family Law case files of racialized immigrant women in Ontario who experienced DV to examine gaps in services and suggest interventions to fill these gaps. The findings reveal that these women and their children experience multiple and intersecting oppressions. Significantly, few existing services recognize the multiple intersecting vulnerabilities, systemic barriers in the host country, or other socio‐political, economic, and cultural realities these women face. Our research reveals a lack of coordination between different institutions providing services to these women. To respond to these gaps, the authors recommend developing interdisciplinary, holistic and culturally‐informed services offered at a single location to support racialized immigrant women and their children as they navigate the complicated path out of the traumatic entrapment of DV.

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)0.000
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 score0.184
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.038
GPT teacher head0.380
Teacher spread0.342 · 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

Citations3
Published2022
Admission routes2
Has abstractyes

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