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Record W3018720701 · doi:10.5216/ree.v21.55653

New lives, new challenges: access to intimate partner violence services for portuguese-speaking immigrant women

2019· article· en· W3018720701 on OpenAlexaffabout
Sepali Guruge, Margareth Santos Zanchetta, Brenda Roche, Stephanie Pedrotti Lucchese

Bibliographic record

VenueRevista Eletrônica de Enfermagem · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsWellesley InstituteToronto Metropolitan University
Fundersnot available
KeywordsDomestic violencePortugueseImmigrationLanguage barrierHealth careFocus groupPsychological resiliencePoison controlSuicide preventionPsychologyPolitical scienceMedicineNursingSociologySocial psychologyEnvironmental health

Abstract

fetched live from OpenAlex


 
 
 
 
 
 
 
 
 
 
 
 Intimate partner violence is a global health issue and the most common form of violence experienced by women. This study explored barriers to accessing help to Intimate partner violence related health services among Portuguese-speaking immigrant women in Toronto, Canada. Exploratory study conducted by a survey and focus group discussions with 12 Portuguese-speaking immigrant women. Results clarify the struggles faced by Portuguese-speaking immigrant women and their pathways to care and help-seeking. Participants reported that the fear of being deported, obtaining evidence of abuse, and lack of language-specific services were the key barriers to seeking help. When available, language-specific community-based services, along with faith and religion, were noted as key factors that supported women’s resilience. Nurses who provide care and services to women who are dealing with Intimate partner violence should rethink the scope of their advocacy actions toward addressing these structural barriers by building alliances with organizations to better serve and protect women in such vulnerable situations.
 
 
 
 
 
 
 
 
 
 
 

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.928
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.052
GPT teacher head0.363
Teacher spread0.311 · 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.

Study designNot applicable
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
Published2019
Admission routes2
Has abstractyes

Explore more

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