Barriers to Safety Planning and Best Practices for Supporting Survivors of Domestic Violence in Rural, Remote, and Northern Regions
Bibliographic record
Abstract
Domestic violence (DV) or intimate partner violence (IPV) is defined as physical, emotional, psychological, or sexual harm in an intimate relationship. In extreme cases, it may culminate in domestic homicide which is defined as the killing of an intimate partner, their children or their family members. Intimate partner violence and domestic homicide is prevalent worldwide. Over ninety-nine thousand reports of DV were made to police in Canada in 2018. According to the Canadian Domestic Homicide Prevention Initiative for Vulnerable Populations, some victims may face greater barriers in receiving assistance on a timely basis such as immigrants and refugees, Indigenous people, children exposed to domestic violence, and those residing in rural, remote, and Northern (RRN) regions. This research seeks to understand the barriers to safety planning and best practices for supporting survivors of DV in RRN regions. This study utilized a qualitative thematic analysis of twenty interviews conducted with survivors of DV in RRN regions. Barriers to safety planning included victim-blaming and patriarchal attitudes, geographical barriers, confidentiality concerns, access to firearms and a distrust in systems. Participants made suggestions for those supporting survivors of DV in RRN regions and included meeting survivors where they are at, providing a non-judgmental space, believing, and validating survivors’ experiences, and providing appropriate resources. Implications for practice among service providers in these areas are discussed
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".