Intimate Partner Violence: What Are Physicians' Perceptions?
Bibliographic record
Abstract
BACKGROUND: Intimate partner violence (IPV) is common in primary care; 11% to 22% of women experienced physical abuse in the past year. Older women experience IPV as well, but it is often undetected. This study examined primary care providers' awareness about IPV in older women, including their screening practices and management. METHODS: Interviews and focus groups were conducted with 44 primary care providers. Thematic analysis was used to identify common themes. RESULTS: Providers fell along a continuum of thoroughness for identifying and managing IPV in older women, ranging from suboptimal to thorough identification of IPV and suboptimal to thorough management of the patient. In addition to the barriers commonly reported about IPV screening in younger women, providers described limited understanding of the diagnoses commonly associated with IPV, frustration with older women's unwillingness to disclose problems and ask for help, and limited community services that accommodate older women with IPV. Providers recommended that communities sponsor public awareness campaigns about IPV as a problem for all women and that aging and IPV agencies work together. CONCLUSIONS: Continued provider training about IPV should include information on identifying older victims and appropriate management options. Participants stressed the importance of community efforts to raise awareness and improve resources available for older women who are victims of IPV.
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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 | 0.007 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".