Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".