Maternal Child Home Visiting Program Improves Nursing Practice for Screening of Woman Abuse
Bibliographic record
Abstract
OBJECTIVE: This study investigated changes in public health nurse practices and the incidence of abuse inquiry and disclosure. DESIGN AND SAMPLE: A retrospective record review of cross-sectional data was collected before and after implementation of the Routine Universal Comprehensive Screening (RUCS) protocol within a maternal child home visiting program. Records of postpartum women receiving a universal home visit within 48 hr of discharge from the hospital were reviewed (pre-RUCS, n=459; post-RUCS, n=485). Also reviewed were the records of women receiving a family assessment for at risk home visiting (pre-RUCS, n=79; post-RUCS, n=66). MEASURES: The variables collected consisted of abuse inquiry, abuse disclosure, and the alone status. RESULTS: Documentation of women's alone status significantly improved for both types of home visits: the 48-hr home visits ( p<.001) and the at risk home visits ( p<.01). Disclosures of abuse significantly increased in both types of home visits ( p<.01). Ensuring privacy by not asking abuse questions if women were not alone during a visit significantly improved ( p<.001). CONCLUSIONS: Implementing a protocol to screen for woman abuse into an existing maternal child home visiting program demonstrated improved practices related to the safety and privacy of women, and an increase in abuse disclosures.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".