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Record W4389192038 · doi:10.22215/etd/2023-15673

Supporting Immigrant Non-Status Survivors of Intimate Partner and Domestic Violence: Recommendations For Canadian Service Providers

2023· dissertation· en· W4389192038 on OpenAlexaffabout
Chandrabarna Saha

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsCarleton University
Fundersnot available
KeywordsService providerImmigrationPreparednessDomestic violenceService (business)Public relationsService delivery frameworkBusinessPolitical scienceNursingSuicide preventionMedicinePoison controlMedical emergencyMarketing

Abstract

fetched live from OpenAlex

This thesis examines the experiences of Gender-Based Violence (GBV) service providers supporting Immigrant Non-Status Survivors of Domestic Violence (INSDV) in Canada.Drawing on interviews with service providers in British Columbia and Ontario, the study explores their preparedness and experiences in serving INSDV.It highlights the challenges service providers face, such as navigating the immigration system and providing effective assistance.Participants offer recommendations to improve the support system, including increasing shelter availability, funding, information dissemination, and enhancing cultural understanding.The findings emphasize the urgent need to address these challenges and ensure equitable support for INSDV in Canada.The study contributes to the understanding of how service providers can better assist INSDV and the systemic gaps that need to be addressed to provide effective services that support their unique needs.asking me to leave Canada at any time.There was no logical explanation of that ever happening, but it had turned into a recurring nightmare, resulting in many sleepless nights spent ensuring meticulous paperwork and documentation to secure stable immigration status.Essentially, living with temporary status demanded constant vigilance over immigration rules and permits, recognizing that any lapse could jeopardize my sense of belonging and stability in the country I now called home.I have now received all the permits and stable immigration status.However, my initial experience has sparked a deep curiosity and interest in the lives and experiences of immigrants in Canada, especially those who reside in the country under temporary or precarious status.This thesis is a product of my personal experience of immigration and my professional engagement with gender-based violence (GBV) service providers in Canada.I am currently employed with Women's Shelters Canada, a non-profit organization that offers a unified, pan-Canadian voice on the issue of violence against women.The organization runs multiple projects to support shelters and transition houses across Canada that support survivors of GBV.One of their initiatives, known as Communities of Practice, brings together service providers from across Canada dedicated to assisting immigrant, refugee, and non-status survivors of GBV.The discussions held during these group meetings were both eye-opening and heart-wrenching.I was inspired to explore and learn more about immigration policies, and the abilities and capacities of service providers when it came to serving Immigrant Non-Status survivors of Domestic Violence (INSDV) and meeting their specific needs.I recognize that there are multiple limitations to using a single category to define and represent a large and diverse group of people.The use of the category (INSDV) in this thesis is not to minimize, simplify, or offer a

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)0.013
metaresearch head score (Gemma)0.022
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: none
Teacher disagreement score0.079
Threshold uncertainty score0.573

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.022
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0350.006
Scholarly communication0.0150.006
Open science0.0050.012
Research integrity0.0060.010
Insufficient payload (model declined to judge)0.0130.001

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 head0.026
GPT teacher head0.372
Teacher spread0.346 · 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
Published2023
Admission routes2
Has abstractyes

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