Service Providers' Perspectives: Reducing Intimate Partner Violence in Rural and Northern Regions of Canada
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) persists as a serious challenge, globally, with regions in Central and Northern Canada reporting the highest rates of shelter use to escape abuse, of sexual assault, and of IPV in the country. Despite research into IPV, barriers and gaps exist in understanding what an effective response to IPV in rural and northern communities should look like. METHODS: To enhance this understanding, qualitative interviews and focus groups with a total of 55 participants were conducted with service providers, including shelter services, victims services, the Royal Canadian Mounted Police, counselors, and others (e.g., psychologists). A grounded theory approach was used to analyze data, with findings illustrated in a schematic that conceptualize the challenges service providers experience. RESULTS: The findings reveal how an IPV environment, characterized by oppression, abuse, and illness, requires transformation into an IPV-free environment, characterized by empowerment, positive social connections, and wellness. As service providers work to influence this transition, they become experts in understanding the sociocultural context, formal services, and informal supports accessible or not for women experiencing IPV. Service providers encourage social media use into service delivery to improve communication; lobby for rural-specific IPV specialists; and recognize isolation as a barrier to seeking out safe shelter and housing, transportation, and economic assistance. CONCLUSION: In order to reduce rates of IPV, the results suggest we must support service providers, document service gaps, and maximize policy change and community action based on IPV as it is experienced in rural and northern regions of Canada.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.029 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".