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Maternal Child Home Visiting Program Improves Nursing Practice for Screening of Woman Abuse

2010· article· en· W1940055091 on OpenAlexaff
Sharon Vanderburg, Leslie Wright, Susan Boston, Greg Zimmerman

Bibliographic record

VenuePublic Health Nursing · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsEssar Steel Algoma (Canada)
Fundersnot available
KeywordsMedicineFamily medicineChild abuseDocumentationPublic healthIncidence (geometry)NursingSuicide preventionPoison controlPsychiatryMedical emergency

Abstract

fetched live from OpenAlex

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.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0000.001
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 head0.042
GPT teacher head0.423
Teacher spread0.381 · 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.

Study designOther design
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

Citations16
Published2010
Admission routes1
Has abstractyes

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