Evidence map›Paper›PMID 28154788›Full record

ArticleClinical psychological science : a journal of the Association for Psychological Science2016

Developing a Risk Model to Target High-risk Preventive Interventions for Sexual Assault Victimization among Female U.S. Army Soldiers.

Amy E Street, Anthony J Rosellini, Robert J Ursano, Steven G Heeringa, Eric D Hill, John Monahan, James A Naifeh, Maria V Petukhova, Ben Y Reis, Nancy A Sampson and 4 more

Abstract read
In one paragraph

Article in Clinical psychological science : a journal of the Association for Psychological Science, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
4.9field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 23 citations in OpenAlex.

  1. Article
  2. State of the Knowledge of VA Military Sexual Trauma Research.Journal of general internal medicine · 2022
    Review
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors at 7 institutions in 2 countries.

Amy E StreetNational Center for PTSD, VA Boston Healthcare System, and Department of Psychiatry, Boston University School of Medicine.
Anthony J RoselliniDepartment of Health Care Policy, Harvard Medical School.
Robert J UrsanoCenter for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine.
Steven G HeeringaInstitute for Social Research, University of Michigan.
Eric D HillDepartment of Health Care Policy, Harvard Medical School.
John MonahanSchool of Law, University of Virginia.
James A NaifehCenter for the Study of Traumatic Stress, Department of Psychiatry, Uniformed Services University School of Medicine.
Maria V PetukhovaDepartment of Health Care Policy, Harvard Medical School.
Ben Y ReisPredictive Medicine Group, Boston Children's Hospital and Harvard Medical School.
Nancy A SampsonDepartment of Health Care Policy, Harvard Medical School.
Paul D BlieseDarla Moore School of Business, University of South Carolina.
Murray B SteinDepartments of Psychiatry and Family Medicine & Public Health, University of California San Diego, and VA San Diego Healthcare System.
Alan M ZaslavskyDepartment of Health Care Policy, Harvard Medical School.
Ronald C KesslerDepartment of Health Care Policy, Harvard Medical School.
Harvard University · USUnifor · NOBoston University · USUniversity of California, San Diego · USUniversity of Michigan–Ann Arbor · USUniversity of South Carolina · USUniversity of Virginia · US

Funding

Modifiable Risk and Protective Factors for Suicidal Behaviors in the US ArmyU01MH087981 · NIMH · HENRY M. JACKSON FDN FOR THE ADV MIL/MED · PI STEIN, MURRAY B., URSANO, ROBERT J. · 2009 to 2013
$65.0M
NIMH NIH HHS U01 MH087981
6 · The paper itself

Abstract

Sexual violence victimization is a significant problem among female U.S. military personnel. Preventive interventions for high-risk individuals might reduce prevalence, but would require accurate targeting. We attempted to develop a targeting model for female Regular U.S. Army soldiers based on theoretically-guided predictors abstracted from administrative data records. As administrative reports of sexual assault victimization are known to be incomplete, parallel machine learning models were developed to predict administratively-recorded (in the population) and self-reported (in a representative survey) victimization. Capture-recapture methods were used to combine predictions across models. Key predictors included low status, crime involvement, and treated mental disorders. Area under the Receiver Operating Characteristic curve was .83-.88. 33.7-63.2% of victimizations occurred among soldiers in the highest-risk ventile (5%). This high concentration of risk suggests that the models could be useful in targeting preventive interventions, although final determination would require careful weighing of intervention costs, effectiveness, and competing risks.

Indexed as

Machine learningmilitary sexual traumaprediction modelraperisk modelsexual assault

Identifiers

PMID28154788
PMCPMC5279920
OpenAlexW2499800352

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.