Nurse Home Visitors' Perspectives of Mandatory Reporting of Children's Exposure to Intimate Partner Violence to Child Protection Agencies
Bibliographic record
Abstract
OBJECTIVES: To examine nurse home visitors' perspectives of and intentions to report children's exposure to intimate partner violence (IPV) in the context of the home visitation setting. DESIGN AND SAMPLE: Cross-sectional study of 532 nurse home visitors in the Nurse-Family Partnership home visitation program. MEASURES: A web-based questionnaire assessing nurse home visitors' support for and attitudes toward mandatory reporting of children's exposure to IPV. Nurses' considerations of what levels of exposure constitute maltreatment and their intended reporting behaviors were also examined. RESULTS: Variability and uncertainty were observed in participants' attitudes as well as in their determinations as to which situations constitute child maltreatment. Most of the sample believed reporting exposure to IPV can help the battered woman (67%) and can protect children (92%), while 56% indicated that reporting can negatively affect the nurse-client relationship. Nurses were more likely to endorse reporting children's exposure to IPV when the child was at greatest risk for being physically injured as a result of IPV. CONCLUSIONS: Training about maltreatment reporting procedures in home visitation programs should focus on the interpretation of child maltreatment laws as well as collaborations with local child protection service agencies to determine if children's exposure to IPV is reportable.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".