Putting the Pieces Together: How Public Health Nurses in Rural and Remote Canadian Communities Respond to Intimate Partner Violence
Bibliographic record
Abstract
Intimate partner violence is a recognized public health problem with direct impacts, including cuts, bruises, and broken bones as well as longer-term effects, including headaches, insomnia, depression, anxiety, substance abuse, and a greater likelihood of developing Post-Traumatic Stress Disorder (PTSD) (Dutton, et al., 2006; Loxton, Scholfield, Hussain, & Mishra, 2006; Plichta, 2004; Romito, Molzan Turan, & Muarchi, 2005). As a result, women who experience IPV use a variety of health care services, including emergency departments, primary care physicians, and public health nurses (Campbell, 2002; Plichta, 2004; Van Hook, 2000).Although rates of IPV identification vary by practice setting, the results suggest that utilization of these services by victims of IPV is common. For example, 13 percent of 1,526 women at 31 outpatient clinics were identified as victims of abuse (Wasson et al., 2000), as were 14 per cent of 2,465 women using a variety of obstetrician/gynecologist offices, emergency departments, primary care offices, pediatrics, and addition recovery (McCloskey, et al., 2005), and 44.3 percent of 399 women at one family medicine clinic (Peralta & Fleming, 2003). Given the prevalence of IPV in these settings, health care providers have a central role in dealing with immediate injuries and other health impacts of IPV. However, it is also crucial for these providers to appropriately identify and refer women to much needed community resources which may prevent further injury and support women in working toward reducing and eliminating violence in their lives. For victims of IPV who live in remote and rural communities, support offered by health care providers is more crucial, as there is often few other resources and services.
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".