Emergency health care providers’ perspectives on providing care to racialized immigrant women experiencing intimate partner violence
Bibliographic record
Abstract
Background: Intimate partner violence (IPV) can impact all Canadian women regardless of socioeconomic status, race, age, or ethnicity. In Canada, prevalence rates are estimated to be 6% to 8%, but many experts argue that rates may be even higher. As well, racialized immigrant women experiencing IPV are likely to face interpersonal and structural barriers to care when accessing services and engaging with health care providers (HCPs). To date, research focusing on emergency department (ED) health care professionals’ perspectives and experiences caring for racialized immigrant women experiencing IPV has been limited. Purpose: The purpose of this study was to explore and better understand ED HCPs’ perspectives on providing care for racialized immigrant women experiencing IPV. The research was conducted at a large urban hospital located in the Lower Mainland of British Columbia (BC). A significant percentage of the population that the hospital serves are foreign-born immigrants originating from India and are of Punjabi-Sikh descent. Given this demographic, it is highly likely that HCPs have cared for Punjabi-Sikh women in the ED. Method: This study used a qualitative descriptive research design. Cultural safety was the theoretical framework used in this study. A convenience sampling approach was used to recruit 5 HCPs. The HCPs consisted of ED nurses, forensic nurses, and social workers who were primarily employed in the ED. This study used individual interviews and a thematic approach to analysis. Findings: The provision of adequate care to racialized immigrant women with IPV issues in the ED was hindered by several issues including the absence of resources, HCPs’ biases, lack of privacy in the ED, long waiting hours, and a lack of time to deal with IPV cases. The study also suggested that ED nurses had to identify IPV cases amongst racialized immigrant women. Implications: Future research should focus on the availability of information and training of ED HCPs to improve care provision for racialized immigrant women. This study also proposed that evidence-based research could help with understanding the unique IPV problems faced by racialized immigrant women, and help to facilitate the provision of efficient care in the ED.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".