Successful Implementation of Universal Woman Abuse Inquiry
Bibliographic record
Abstract
OBJECTIVE: This study investigated a year-long professional development strategy to improve Public Health Nurse (PHN) documented abuse inquiry among low-risk postpartum women. Strategies included workshops and small group work at regular intervals. DESIGN: A retrospective chart audit of cross-sectional data collected as part of the Healthy Babies/Healthy Children Program was conducted 1 year before and after the introduction of the Routine Universal Comprehensive Screening (RUCS) Program. SAMPLE: Charts of all postpartum women who lived in one Ontario county (Canada) and who received a PHN home visit were reviewed (pre-RUCS, n=1,151; post-RUCS, n=1,193). MEASUREMENT: Information regarding mother's age group and parity, month of PHN visit, abuse inquiry, and whether or not the woman was a single parent was abstracted. RESULTS: Originally, there was documentation of abuse inquiry on only 0.8% of low-risk postpartum client charts. Women aged <20 years and single parents were significantly (p<.001) more likely to be asked, suggesting case-finding among PHNs. Post-RUCS abuse inquiry increased to 20.5% with no demographic differences between those groups asked. CONCLUSION: Policy changes providing specific expectations and documentation cues can improve routine abuse inquiry. New policies can be effectively combined with existing programs and infrastructure, facilitating the longer term success of new initiatives.
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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 | 0.011 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".