Disclosing abuse: The voices of Spanish-speaking women in Canada
Bibliographic record
Abstract
This study examined the experiences of Spanish-speaking Latin American immigrant women abused by their intimate partners. The focus of the study explored from a Canadian context the experiences of Latin-American immigrant women regarding the disclosure of abuse and elicited data on how helping professionals can better support immigrant women in disclosing and seeking help for intimate partner violence. A participatory action approach utilizing in-depth interviewing with helping professionals as key informants, and focus group methodology with Latin-American women was employed to better understand the barriers and facilitators of disclosure of intimate partner violence. Thematic analysis highlighted the cultural and language barriers faced by women, fragmentation of the legal and social service systems, the complexities involved for immigrant women in maneuvering their way through these systems, fear of their partners retaliation, losing their children and compromising their immigration application, and distrust of dealing with authorities in the Canadian service system. This study shows the need for immigrant women to be able to secure safety and better access to supportive social services, and for the social service network to find ways to provide these more effectively. These findings have important implications for social work practice and policy in working with this often overlooked population in Canada: the Latin American community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.027 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".