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Successful Implementation of Universal Woman Abuse Inquiry

2006· article· en· W1965834509 on OpenAlexaffabout
Denise Grafton, B Wright, Iris Gutmanis, Susan Ralyea

Bibliographic record

VenuePublic Health Nursing · 2006
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsSt Joseph's Health CareSt. Joseph's HospitalMiddlesex London Health UnitWestern University
Fundersnot available
KeywordsDocumentationAuditMedicineNursingFamily medicinePublic healthChild abusePsychologySuicide preventionPoison controlMedical emergency

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.482
Threshold uncertainty score0.982

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.415
Teacher spread0.354 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2006
Admission routes2
Has abstractyes

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