Sociodemographic Information, Aversive and Traumatic Events, Offence-Related Characteristics, and Mental Health of Delinquent Women in Forensic-Psychiatric Care in Switzerland.
Bibliographic record
Abstract
The present study describes a much understudied group-namely, female prisoners under forensic-psychiatric care in the German-speaking part of Switzerland-to improve understanding of their risks and their needs. Data were derived from internal databases of a Forensic-Psychiatric Service. Data were collected in the form of their sociodemographic characteristics, prevalence of aversive and traumatic events, type of offence committed, and mental health conditions. Based on a full-sample approach, a total of 1,571 files were analysed. Results reveal that two thirds of the participants were not in a stable relationship, more than half did not complete a school degree, and three quarters were without stable employment prior to their incarceration. Two thirds were mothers and about one third did not grow up with their parents. Almost half grew up with an alcohol abusing parent, about half experienced violence and/or neglect in childhood, and about a quarter of the cases sexual abuse. About 95% had a mental health diagnosis according to International Classification of Diseases-Version 10 (ICD-10), and the most prevalent mental and behavioural disorder was due to psychoactive substance abuse. The most frequent offence type was drug-related crimes. Women convicted for drug-related crimes were more likely to have an ICD-10 F1 disorder compared with those convicted for other crimes. Conversely, women with violent offences were less likely to suffer from ICD-10 F1 disorder than those who had committed nonviolent offences. Findings have implications for practitioners and policy makers, and contribute to the cycle of violence theory discussion. In conclusion, future research areas are suggested.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".