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Record W4401955298 · doi:10.2196/59918

Implementation and Impact of Intimate Partner Violence Screening Expansion in the Veterans Health Administration: Protocol for a Mixed Methods Evaluation

2024· article· en· W4401955298 on OpenAlexvenueno aff
Galina A. Portnoy, Mark Relyea, Melissa E. Dichter, Katherine M. Iverson, Candice Presseau, Cynthia Brandt, Melissa Skanderson, LeAnn E. Bruce, Steve Martino

Bibliographic record

VenueJMIR Research Protocols · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicIntimate Partner and Family Violence
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesU.S. Department of Veterans Affairs
KeywordsDomestic violenceMedicineFocus groupImplementation researchHealth careNursingQualitative propertyObservational studyPoison controlPublic healthQualitative researchProtocol (science)Family medicineSuicide preventionEnvironmental healthPsychological interventionAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Intimate partner violence (IPV) is a significant public health problem with far-reaching consequences. The health care system plays an integral role in the detection of and response to IPV. Historically, the majority of IPV screening initiatives have targeted women of reproductive age, with little known about men's IPV screening experiences or the impact of screening on men's health care. The Veterans Health Administration (VHA) has called for an expansion of IPV screening, providing a unique opportunity for a large-scale evaluation of IPV screening and response across all patient populations. OBJECTIVE: In this protocol paper, we describe the recently funded Partnered Evaluation of Relationship Health Innovations and Services through Mixed Methods (PRISM) initiative, aiming to evaluate the implementation and impact of the VHA's IPV screening and response expansion, with a particular focus on identifying potential gender differences. METHODS: The PRISM Initiative is guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) and Consolidated Framework for Implementation Research (CFIR 2.0) frameworks. We will use mixed methods data from 139 VHA facilities to evaluate the IPV screening expansion, including electronic health record data and qualitative interviews with patients, clinicians, and national IPV program leadership. Quantitative data will be analyzed using a longitudinal observational design with repeated measurement periods at baseline (T0), year 1 (T1), and year 2 (T2). Qualitative interviews will focus on identifying multilevel factors, including potential implementation barriers and facilitators critical to IPV screening and response expansion, and examining the impact of screening on patients and clinicians. RESULTS: The PRISM initiative was funded in October 2023. We have developed the qualitative interview guides, obtained institutional review board approval, extracted quantitative data for baseline analyses, and began recruitment for qualitative interviews. Reports of progress and results will be made available to evaluation partners and funders through quarterly and end-of-year reports. All data collection and analyses across time points are expected to be completed in June 2026. CONCLUSIONS: Findings from this mixed methods evaluation will provide a comprehensive understanding of IPV screening expansion at the VHA, including the implementation and impact of screening and the scope of IPV detected in the VHA patient population. Moreover, data generated by this initiative have critical policy and clinical practice implications in a national health care system. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/59918.

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 imitation

Not 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.

metaresearch head score (Codex)0.123
metaresearch head score (Gemma)0.092
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.123
Threshold uncertainty score0.652

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1230.092
Meta-epidemiology (narrow)0.0060.004
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0040.005
Science and technology studies0.0060.004
Scholarly communication0.0060.004
Open science0.0050.005
Research integrity0.0060.008
Insufficient payload (model declined to judge)0.0520.008

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 head0.499
GPT teacher head0.736
Teacher spread0.237 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreProtocol

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

Citations4
Published2024
Admission routes1
Has abstractyes

Explore more

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