Using a cross-sectional survey design from 11 public tertiary hospitals (a specialist hospital, four Chinese medicine hospitals, and six general hospitals) in the urban areas of Heilongjiang, we determined the nature of workplace violence that medical staff have encountered in Chinese hospitals and identified factors associated with those experiences of violence. A total of 1129 health workers participated. The specialist hospital had the highest prevalence of physical violence (35.4%), while the general hospitals had the highest prevalence of non-physical violence (76%). Inexperienced medical staff (p < 0.001) were more likely to suffer non-physical violence than physical violence in Chinese medicine hospitals compared to experienced staff. Medical units (p = 0.001) had a high risk of non-physical violence, while surgical units (p = 0.005) had a high risk of physical violence. In general hospitals, staff with higher levels of anxiety about workplace violence were more vulnerable to both physical violence (1.67, 95% CI 1.36–2.10) and non-physical violence (1.309, 95% CI 1.136–1.508) compared to those with lower levels of anxiety, while rotating shift workers had a higher odds of physical violence (2.2, 95% CI 1.21–4.17) and non-physical violence (1.65, 95% CI 1.13–2.41) compared to fixed day shift workers. Thus, prevention should focus not only on high-risk sections of hospitals, but also on the nature of the hospital itself.

We hypothesized that cerebral white matter deficits in schizophrenia (SZ) are driven in part by accelerated white matter aging and are associated with cognitive deficits. We used machine learning model to predict individual age from diffusion tensor imaging features and calculated the delta age (Δage) as the difference between predicted and chronological age. Through this approach, we translated multivariate white matter imaging features into an age-scaled metric and used it to test the temporal trends of accelerated aging-related white matter deficit in SZ and its association with the cognition. Followed feature selection, a machine learning model was trained with fractional anisotropy values in 34 of 43 tracts on a training set consisted of 107 healthy controls (HC). The brain age of 166 SZs and 107 HCs in the testing set were calculated using this model. Then, we examined the SZ-HC group effect on Δage and whether this effect was moderated by chronological age using the regression spline model. The results showed that Δage was significantly elevated in the age >30 group in patients (p < 0.001) but not in age ⩽ 30 group (p = 0.364). Δage in patients was significantly and negatively associated with both working memory (β = −0.176, p = 0.007) and processing speed (β = −0.519, p = 0.035) while adjusting sex and chronological age. Overall, these findings indicate that the Δage is elevated in SZs and become significantly from middle life stage; the increase of Δage in SZs is associated with the decline neurocognitive performance.