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Record W3045087789 · doi:10.1177/1079063220940305

A Comparison of Risk Factors Among Discharged Military Veterans and Civilians Involuntarily Hospitalized Under California’s <i>Sexually Violent Predator</i> Act

2020· article· en· W3045087789 on OpenAlexaff
Sarah G. Paden, Allen Azizian, Shoba Sreenivasan, Jim McGuire, Stephanie Brooks Holliday, Michael C. Seto

Bibliographic record

VenueSexual Abuse · 2020
Typearticle
Languageen
FieldPsychology
TopicPsychopathy, Forensic Psychiatry, Sexual Offending
Canadian institutionsRoyal Ottawa Mental Health Centre
Fundersnot available
KeywordsPsychiatryInjury preventionMilitary personnelPoison controlPsychologyOccupational safety and healthSuicide preventionMedicineSex offenseClinical psychologyMedical emergencySexual abuse

Abstract

fetched live from OpenAlex

While military veterans have a lower overall rate of incarceration for criminal offenses than civilians, they have a higher rate of incarceration for violent sexual offenses. Despite military veteran overrepresentation among individuals adjudicated for violent sexual offenses, little is known about their risk factors for sexual offending. This study compared military veterans and civilians who had been involuntarily hospitalized and discharged pursuant to California's Sexually Violent Predator Act. Pedophilic disorder appeared nearly twice as often among veterans than civilians (62.7% vs. 38.7%), whereas antisocial personality disorder was twice as common among civilians compared to veterans (48.1% vs. 23.9%). Consistent with the result for pedophilic disorder, veterans were more likely to target male victims age 13 and below, while civilians tended to target female victims over the age of 13. The results suggest different risk profiles for veterans compared to civilians who have been convicted of sexually violent offenses.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.062
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.034
GPT teacher head0.305
Teacher spread0.272 · 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 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

Citations4
Published2020
Admission routes1
Has abstractyes

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