Barriers to Screening for Intimate Partner Violence
Bibliographic record
Abstract
BACKGROUND: Health care providers play a vital role in the detection of intimate partner violence among their patients. Despite the recommendations for routine intimate partner violence screening in various medical settings, health care providers do not routinely screen for intimate partner violence. The authors wanted to identify barriers to intimate partner violence screening and improve the understanding of intimate partner violence screening barriers among different health care providers. METHODS: The authors conducted a systematic review to examine health care providers' perceived barriers to screening for intimate partner violence. By grouping the studies into two time periods, based on date of publication, they examined differences in the reported barriers to intimate partner violence screening over time. RESULTS: The authors included a total of 22 studies in this review from all examined sources. Five categories of intimate partner violence screening barriers were identified: personal barriers, resource barriers, perceptions and attitudes, fears, and patient-related barriers. The most frequently reported barriers included personal discomfort with the issue, lack of knowledge, and time constraints. Provider-related barriers were reported more often than patient-related barriers. CONCLUSIONS: Barriers to screening for intimate partner violence are numerous among health care providers of various medical specialties. Increased education and training regarding intimate partner violence is necessary to address perceptions and attitudes to remove barriers that hinder intimate partner violence screening by health care providers.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".