We explored interactions between criminogenic risk, discharge routes and reconvictions in a contemporary cohort of people following medium-secure care. Data from the Offenders Index were used to estimate actuarial risk for reconviction and to determine 6-year reconviction outcomes for 205 consecutive discharges from a single medium-secure service (1999–2017). Clinical details were sourced from healthcare records.

Preadmission risk was lowest for people discharged to open settings, but equivalent between those discharged to secure care or prison. Both violent and any offence reconviction outcomes were more frequent for people discharged to prison than either locked units or open conditions at all the three follow-up points (2, 4 and 6 years).

Prison discharge negated large reductions in reconvictions observed with alternative discharge routes. Actuarial risk assessment has a place in evaluating the effectiveness public protection provided by secure services.

Community forensic mental health services (CFMHS) in England were developed to reduce reliance on hospital care for this population, but provision varies. It is unclear whether standard setting work has increased consistency. Freedom of information requests were therefore sent to 50 National Health Service Trusts in England, to examine the population, staffing, treatment provision and organisation of CFMHS.

Data were provided for 32 CFMHS, of which 59% were part of secure hospital-based services and 41% were standalone services working in parallel with general services. There was variation in aspects including professional composition, functions, the settings from where CFMHS accept referrals and long-term approach to patients subject to special restrictions under the Mental Health Act 1983.

CFMHS continue to vary, especially in their interface with services other than forensic hospitals. This may impede standard setting and empirical evaluation. Different approaches to centralising oversight may be needed for standardisation.

Disaster risk assessment is essential in territories increasingly affected by climate-related hazards. This study applies a sequential methodology for quantifying and assessing disaster risk using open-source information, focusing on Coquimbo, Chile, a coastal city with high exposure.

A 7-step model was developed, integrating sectorization by Neighborhood Units, hazard identification, recurrence analysis (2000-2024), and the evaluation of vulnerabilities, capacities, and exposure. Hazards included earthquakes, tsunamis, droughts, wildfires, and landslides. Resilience was calculated through weighted vulnerability and capacity variables, while exposure was estimated using infrastructure values. Disaster risk was computed as the product of hazard, resilience, and exposure.

Coquimbo shows heterogeneity. UV06 Sindempart, UV10 El Llano, and UV15 La Herradura demonstrated capacities, while UV12 El Culebrón and UV17–UV18 Tierras Blancas showed the highest vulnerabilities. Coastal UVs such as UV05 Baquedano and UV13 Guayacán concentrate exposure due to infrastructure and population density. Earthquakes and tsunamis were identified as the most recurrent hazards. The methodology generated 150 risk estimates across 30 Neighborhood Units.

The methodology enables a structured, replicable evaluation of disaster risk, even with limited data. Its application in Coquimbo highlights territorial disparities that can guide prioritization of resilience strategies, early-warning systems, and evidence-based planning.

Sexual violence in psychiatric in-patient care has received increasing attention following persistent evidence of sexual violence and harassment on wards. However, patients’ subjective experiences remain under-examined, limiting the evidence base to inform safeguarding, gender-sensitive design and trauma-informed practice.

To synthesise qualitative evidence on patients’ experiences of sexual violence and perceived risk within psychiatric in-patient settings.

We conducted a systematic review and qualitative synthesis following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines (PROSPERO registration no. CRD42024595945). MEDLINE, Embase and PsycINFO were searched from inception to October 2024, supplemented by reference list screening and citation tracking. Inclusion criteria were peer-reviewed qualitative studies reporting patients’ first-hand accounts of sexual violence or perceived sexual safety in psychiatric in-patient care. The quality of the included studies was assessed using the Critical Appraisal Skills Programme checklist, and data were analysed by thematic synthesis.

Six studies published between 1998 and 2025 met the inclusion criteria, with most focusing on female patients in mixed-gender wards. Three overarching themes were developed: (a) ‘a culture of permissiveness and dismissal’ – patients downplayed harassment and abuse and staff routinely dismissed concerns; (b) ‘everyday fear, hypervigilance and resistance’ – the constant threat of harm generated chronic distress, with safeguarding responsibility shifted onto the patients; and (c) ‘gendered power dynamics in open or mixed-gender spaces’ – open ward layouts, inadequate boundaries and legal detention compounded vulnerability to harm.

Sexual violence in psychiatric in-patient care is enabled by ward cultures that normalise harm, weak safeguarding and gendered power imbalances. Urgent action is needed to implement trauma-informed, gender-sensitive practices and secure spatial boundaries and consistent incident responses, alongside policies that enable safe disclosure and accountability.

Suicide is a major public health challenge requiring early detection of suicidal ideation (SI). Traditional direct questioning methods suffer from stigma and disclosure bias, failing to identify many at-risk individuals. While machine learning (ML) models show promise, most lack external validation. Indirect screening, using psychosocial data rather than direct SI questions, offers a scalable alternative. This study aimed to externally validate an indirect, ML-based SI screening tool. We tested if a model trained on a Slovenian general population sample retained predictive accuracy when applied to an independent Croatian sample during a period of societal stress (pandemic and earthquakes), assessing performance across age and gender subgroups.

A logistic regression model was trained on a Slovenian sample (N = 2,989) and validated on a Croatian sample (N = 2,364). The model used only indirect predictors, including sociodemographics, life satisfaction, behavioral changes, and Brief COPE subscales. The target outcome was the presence of SI (SIDAS score > 0). Performance was measured by the area under the receiver operating characteristic curve (AUROC).

The model demonstrated strong external validity on the entire Croatian sample, achieving an AUROC of 0.80. Performance remained robust across subgroups: males (AUROC = 0.83), females (AUROC = 0.79), younger adults (AUROC = 0.77), and older adults (AUROC = 0.81). Self-blame, behavioral disengagement, and relationship dissatisfaction were key predictors.

An indirect, ML-based screening tool can reliably identify SI risk in the general population. The model demonstrated strong cross-national transferability and resilience during a societal crisis, proving it is a feasible and valid strategy for population-level prevention.