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Record W4242813073 · doi:10.32920/ryerson.14666898.v1

Social support needs and help seeking behaviours among Spanish-speaking Canadian immigrant women who have experienced intimate partner violence (IPV)

2021· preprint· en· W4242813073 on OpenAlexaffabout
Angela Cerdena D’Unian

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsUniversity of GuelphToronto Metropolitan University
FundersDivision of Graduate Education
KeywordsDomestic violenceImmigrationIntersectionalityAbusive relationshipSocial supportFocus groupPsychologyQualitative researchSocial psychologyGender studiesPoison controlSuicide preventionPolitical scienceSociologyMedicineEnvironmental health

Abstract

fetched live from OpenAlex

There is considerable research about women who have experienced Intimate Partner Violence (IPV) in the Canadian literature. However, most of these studies have focused on IPV among Canadian-born women. Immigrant women who make the decision to seek help for IPV have received less attention in the research-based literature in Canada. This qualitative study examined the IPV experiences of 10 Spanish-speaking immigrant women in Canada, all from the Greater Toronto Area (GTA). The main focus was to examine the intersectionality between social support and help seeking behaviours for IPV. Results indicated that Spanish-speaking immigrants in Canada utilized both formal and informal sources of support for IPV. The importance of continuous support as a factor preventing women from returning to an abusive relationship was consistently reported by participants. Implications of the study findings and directions for future research are further discussed in this manuscript.

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)0.001
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.175

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0040.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.025
GPT teacher head0.312
Teacher spread0.287 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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