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Record W4243832628 · doi:10.21694/2578-5508.17005

Sexual Exploitation of Women with Schizophrenia

2017· article· en· W4243832628 on OpenAlexaff
Mary V. Seeman

Bibliographic record

VenueAmerican Research Journal of Addiction and Rehabilitation · 2017
Typearticle
Languageen
FieldPsychology
TopicSexuality, Behavior, and Technology
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSchizophrenia (object-oriented programming)PsychologyPsychiatry

Abstract

fetched live from OpenAlex

Background: Vulnerable populations are sometimes targets for violence and sexual exploitation; this has been reported with respect to women with serious mental illnesses such as schizophrenia. Aim: The aim of this review is to summarize the recent literature on this topic and to outline promising strategies for prevention. Method: Relevant search terms were used in the Google Scholar database from 2000 to the present, and 50 English language papers were selected for review. Results: Women with schizophrenia are targets for sexual harassment and violence, not only domestically, but also on the street, and in institutions. Perpetrators can be intimate partners, strangers, and hospital or prison staff. Women with schizophrenia are sometimes sextrafficked. They are vulnerable because of isolation, passivity, cognitive defects, psychotic symptoms, substance abuse, homelessness, and poverty, and, as a consequence of exploitation, they suffer shame and guilt, increased severity of psychotic symptoms, and increased risk of sexually transmitted disease, unwanted pregnancy, and abortion. On psychiatric assessment, they are seldom asked about the practice of survival sex or about sexual exploitation, and seldom disclose these aspects of their life. Clinical programs are beginning to become available that address self-stigma, educate about risk factors for sexual exploitation and teach safety and self-defense as well as conflict resolution. Psychiatric services are instituting screening procedures for employees, appropriate staff education, and improvements in surveillance measures and policies. Conclusion: Because of increasing awareness of harassment and exploitation of women in general, there is mounting concern about women made more vulnerable than others as a result of severe mental illness.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.826
Threshold uncertainty score0.867

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.002
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 head0.043
GPT teacher head0.407
Teacher spread0.364 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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