Wang T, Wang L, Zhao P, Sun JJ, Gao LN, Li J, Zhao YQ. Application research on the predictive model for violent behavior in hospitalized patients with severe mental disorders. World J Psychiatry 2026; 16(2): 112575 [DOI: 10.5498/wjp.v16.i2.112575]
Corresponding Author of This Article
Ya-Qin Zhao, Master, Chief Physician, Deputy Director of the Mental Health Prevention Center, Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching Hospital of Yangzhou University, No. 2 Wutaishan Road, Yangzhou 225003, Jiangsu Province, China. 114380028@qq.com
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Psychiatry
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Observational Study
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Feb 19, 2026 (publication date) through Feb 2, 2026
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World Journal of Psychiatry
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Wang T, Wang L, Zhao P, Sun JJ, Gao LN, Li J, Zhao YQ. Application research on the predictive model for violent behavior in hospitalized patients with severe mental disorders. World J Psychiatry 2026; 16(2): 112575 [DOI: 10.5498/wjp.v16.i2.112575]
Ting Wang, Lin Wang, Ping Zhao, Jiao-Jiao Sun, Li-Ni Gao, Jia Li, Department of Outpatient, Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching Hospital of Yangzhou University, Yangzhou 225003, Jiangsu Province, China
Ya-Qin Zhao, Department of Psychiatry, Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching Hospital of Yangzhou University, Yangzhou 225003, Jiangsu Province, China
Author contributions: Wang T, Wang L, Zhao P, Sun JJ, and Gao LN contributed to original manuscript draft; Wang T, Wang L, and Zhao YQ contributed to funding support; Wang T, Zhao P, Sun JJ, and Li J contributed to investigation; Wang L, Zhao P, and Gao LN contributed to the methodology; Zhao P and Gao LN contributed to software; Sun JJ participated in the data curation; Gao LN and Zhao YQ participated in conceptualization; Li J and Zhao YQ contributed to supervision, revised and edited the manuscript. Wang T and Wang L contributed equally to this manuscript as co-first authors; Li J and Zhao YQ contributed equally to this manuscript as co-corresponding authors. All authors have read and approved the final manuscript.
Supported by the Hospital Project Funding Fund of Yangzhou Wutaishan Hospital of Jiangsu Province, No. WTS2025009 and No. WTS2022004; and Yangzhou City Basic Research Program (Joint Special Project) - Health and Wellness Category, No. 2025-3-33 and No. 2023-4-4.
Institutional review board statement: This study earned the approval of the Ethics Committee of the Yangzhou Wutaishan Hospital of Jiangsu Province (approval No. WTSLL2025011).
Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.
Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Data sharing statement: The datasets generated and/or analysed during the current study are not publicly available due to individual privacy but are available in summary/group level form from the corresponding author on reasonable request.
Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Ya-Qin Zhao, Master, Chief Physician, Deputy Director of the Mental Health Prevention Center, Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching Hospital of Yangzhou University, No. 2 Wutaishan Road, Yangzhou 225003, Jiangsu Province, China. 114380028@qq.com
Received: July 31, 2025 Revised: September 20, 2025 Accepted: November 10, 2025 Published online: February 19, 2026 Processing time: 183 Days and 17.8 Hours
Abstract
BACKGROUND
To understand the current situation of violent behavior among hospitalized patients with severe mental disorders (SMDs), analyze its influencing factors, establish a predictive model and draw a nomogram, providing screening tools for medical staff to accurately identify SMDs who have violent behavior and the direction of early intervention.
AIM
To investigate the determinants of violent actions in hospitalized patients with SMDs.
METHODS
This research included 440 inpatients with SMDs who were admitted to the Wutaishan Hospital from January 2025 to June 2025. Data collection and analysis aimed to pinpoint independent contributors linked to aggression in this patient group. An advanced logistic regression analysis with multiple variables was performed using R, followed by the creation of a line chart to display the forecast outcomes of the model.
RESULTS
Of 120 patients exhibited violent behavior (incidence rate = 27.30%). Education level, cigarette smoking, length of hospitalization, age, psychotic symptoms based on the Brief Psychiatric Rating Scale, and C-reactive protein were independent risk factors for violent behavior. Education level and age served as protective elements among the factors analyzed. The receiver operating characteristic curve area for the training and test sets was calculated to be 0.94 and 0.93, respectively. The calibration graph demonstrated that the model was accurately adjusted. The clinical decision curve demonstrated that the model provided significant practical benefits.
CONCLUSION
The predictive mode provided a valuable theoretical basis for ward staff to identify inpatients with SMDs at elevated risk of aggression in the early phase.
Core Tip: This study constructed a predictive model by identifying the independent influencing factors of violent behavior in hospitalized patients with severe mental disorders, internally validated the model, and visually presented the model through a nomogram. The result of research shows that: Education level, cigarette smoking, length of hospitalization, age, Brief Psychiatric Rating Scale, and C-reactive protein were independent risk factors. The predictive model provided a valuable theoretical basis for ward staff to identify inpatients with severe mental disorders at elevated risk of aggression in the early phase.
Citation: Wang T, Wang L, Zhao P, Sun JJ, Gao LN, Li J, Zhao YQ. Application research on the predictive model for violent behavior in hospitalized patients with severe mental disorders. World J Psychiatry 2026; 16(2): 112575
A severe mental disorder (SMD) denotes a state in which intense mental health symptoms lead to considerable dysfunction. A SMD has been described as a condition marked by severe psychiatric symptoms that substantially disrupt an individual’s social functioning ability[1]. This description encompasses challenges to fully understand one’s health status and factual reality, as well as efficient handling personal matters. A SMD includes conditions, like schizophrenia, bipolar disorder, paranoid-type mental disorders, schizoaffective disorders, and mental disorders linked to epilepsy, as well as intellectual disabilities with psychiatric issues, which affect the ability of a patient to function socially. Individuals with a SMD often face emotional instability, which increases the likelihood of exhibiting aggressive behavior. Individuals diagnosed with a SMD often show emotional volatility, which increases the probability of affecting themselves, others, and the environment. Violent conduct involves intentional actions that use force or power to intimidate or confrontation with aggression, whether directed at oneself, others, or objects in the vicinity, which is a significant risk to safety[2]. The prevalence of violent events in psychiatric hospital environments is a common challenge that frequently results in unpredictable situations, potentially affecting specific individuals, groups, or the broader community, and poses a risk to personal safety and public order[3]. Violent incidents within psychiatric hospitals are frequent and often lead to unexpected serious consequences. Consequently, psychiatric healthcare providers must promptly identify high-risk individuals, establish preventive strategies, act efficiently in response to violence, and alleviate the adverse effects linked to these actions[4].
Although violent actions can be unpredictable and beyond the control of nurses[5], it is important for nurses to identify significant risk factors that lead to patient aggression[6]. Factors, such as an individual’s psychological status, along with encounters involving hallucinations or delusions, can lead to sensations of danger or anxiety, thereby raising the likelihood of aggressive behavior. The American Nurses Association has questioned whether workplace violence can be prevented from a mental health care perspective[7]. Various global investigations have detailed episodes of workplace violence and the consequences[8,9], highlighting the diverse factors that provoke aggression from patients. Nevertheless, there has been scant research dedicated to pinpointing factors that lead to violent behavior among psychiatric inpatients[10]. A thorough review involving violence within mental health workplaces carried out in China identified factors linked to patients, nursing methods, and social and environmental factors, although a further in-depth analysis is warranted[11]. The following crucial question emerged: Who holds the final accountability for the outcomes of aggressive actions carried out by individuals with SMDs? Although hospitals are proactive in reducing patient aggression, nurses working on the front lines must utilize comprehensive evaluation skills, especially when managing psychiatric inpatients. Indeed, it is important to carefully observe both verbal and non-verbal cues that may indicate warning signs and implement methods for identifying possible violent behavior in an early stage. Proactive strategies for prevention are essential to ensure a secure environment. Domestic research on violence prediction models has primarily concentrated on patients with various mental disorders, and the investigation of biomarkers for predicting violent behavior has evolved into a multidimensional framework. At the genetic level, polymorphisms in genes such as monoamine oxidase A and dopamine receptor type 2 influence neurotransmitter systems and are associated with impaired impulse control and increased aggression. Genome-wide association studies have further identified links between dopamine receptor gene variants and predispositions to antisocial or criminal behaviors[12]. At the physiological level, dysregulation of the autonomic nervous system - evidenced by reduced heart rate variability and altered cortisol rhythms - has been consistently associated with impaired stress regulation in individuals with mental illness. Neuroimaging studies have revealed structural and functional abnormalities in key brain regions, including the amygdala and prefrontal cortex, offering empirical insights into the neural underpinnings of aggressive tendencies[13]. Collectively, these findings contribute to a multilevel biomarker model encompassing genetic, neural, and physiological domains. However, the role of inflammatory mechanisms within this framework remains underexplored. This study addresses this gap by focusing on C-reactive protein (CRP), a well-established marker of inflammation. Prior evidence indicates that CRP levels are significantly elevated in patients with intermittent explosive disorder who exhibit frequent violent behavior, compared to those with irritability but no aggression[14]. Furthermore, animal studies have demonstrated that pro-inflammatory proteins can directly enhance aggressive behaviors[15]. These findings suggest that inflammatory markers complement existing biomarkers and strengthen the predictive validity of violence risk models. Accordingly, this study incorporates CRP as a potential risk factor, to analyze the distinct elements that impact aggressive actions in hospitalized individuals with SMDs and create a model for predicting such behavior. The goal of the study was to support medical personnel in the timely detection of high-risk patients, allowing for customized actions that match patient requirements and prioritize safety during inpatient treatment.
MATERIALS AND METHODS
Participants
The study used a cross-sectional methodology and involved inpatients at the Yangzhou Wutaishan Hospital of Jiangsu Province during the period extending from January 2025 to June 2025 who were diagnosed with a SMD. The inclusion criteria were as follows: (1) Patients who met the diagnostic requirements outlined in the 10th edition of the International Classification of Diseases; (2) Mental health conditions, including schizophrenia, bipolar disorder, paranoid psychosis, schizoaffective disorder, psychiatric disorders associated with epilepsy, and intellectual disabilities that manifest with psychiatric symptoms; and (3) Patient’s willingness to be involved in the study, whether directly or via a family representative, with properly executed informed consent. The exclusion criteria were as follows: (1) Severe physical disorders or co-morbidities; (2) Inability of the patient and/or the caregiver to finish the required evaluation tools; and (3) Incomplete or missing medical records.
Study design
The sample size was calculated to be 5-10 times larger than the number of identified influencing factors based on logistic regression analysis[16]. The projected necessary sample size was estimated to range between 105 and 209 participants considering a 10% rate of invalid responses. The study ultimately included 440 inpatients who met the eligibility criteria. The dataset was split into two groups at random at a 7:3 ratio. This resulted in 308 patients designated as the modeling group, while the other 132 patients comprised the validation group.
Data collection procedure and tool
The study group formulated a comprehensive data collection form to gather patient socio-demographic details, including age, gender, level of education, domicile, marital status, duration of hospitalization, family history, and other relevant information. The Brief Psychiatric Rating Scale (BPRS)[17] was utilized to measure the intensity of psychiatric symptoms in individuals diagnosed with mental health conditions. The evaluation was carried out by experts in the field of mental health. The 18-item BPRS version was used and encompassed 2 negative symptom evaluations (e.g., anxiety, depression, and energy depletion) and 3 positive symptom evaluations (e.g., disorders of thought, excitement, hostility, and suspicion) measured with a 7-point scale. The overall score indicated the intensity of the disease, while the factor scores illustrated the clinical features of the patients’ symptoms. A higher score reflected more severe symptoms[18].
Statistical analysis
Statistical analysis of the data was performed utilizing SPSS (version 25.0). Variables with a normal distribution were described using the mean ± SD. Conversely, variables with a skewed distribution were characterized by the median and interquartile range. The categorical data were depicted using quantities and percentages. An independent samples t-test was performed on continuous variables with a normal distribution to compare groups, while the Mann-Whitney U test was utilized for variables that did not follow a normal distribution. The χ2 was used to examine datasets categorized into distinct groups. Logistic stepwise regression analysis was used to pinpoint independent risk elements linked to aggressive conduct in patients. R software (version 4.1.2), a model for forecasting risk, was created with the logistic regression outcomes visually represented by a line graph. A receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were utilized to evaluate model performance. Furthermore, the model underwent internal assessment using the bootstrap resampling technique. P < 0.05 was considered statistically significant.
RESULTS
General information
Of the 440 hospitalized individuals with SMDs, 120 (27.30%) exhibited aggressive conduct. Among the 440 inpatients, 76 (24.68%) comprised the modeling cohort and 44 (33.33%) were assigned to the validation cohort. There was no meaningful statistical variation in demographic factors, like age, gender, location of residence, and rates of aggressive actions between the sample and testing group (P > 0.05; Table 1).
Table 1 Comparison of general data between the modeling and verification groups of hospitalized patients with severe mental disorders, n (%).
Item
Model group (n = 308)
Validation group (n = 132)
χ2/Z/t
P value
Act of violence
3.49
0.06
No
232 (75.32)
88 (66.67)
Yes
76 (24.68)
44 (33.33)
Gender
0.02
0.88
Male
154 (50.00)
65 (49.24)
Female
154 (50.00)
67 (50.76)
Diagnose
3.09
0.38
Schizophrenia
178 (57.79)
79 (59.85)
Bipolar affective disorder
42 (13.64)
23 (17.42)
Paranoid disorder
42 (13.64)
19 (14.39)
Schizoaffective disorder
38 (12.34)
10(7.58)
Mental disorders caused by epilepsy
6 (1.95)
1 (0.76)
Mental retardation with mental disorder
2 (0.65)
0 (0)
Educational background
0.35
0.84
Primary school and below
81 (26.30)
38 (28.79)
Middle school/high school/vocational high school/vocational school
Single factor analysis of violent behavior in patients with SMDs in the modeling module
The findings from the univariate analysis within the modeling module indicated that patients with SMDs involved in violent actions exhibited notable statistical differences with respect to diagnosis, educational background, employment, marital status, living situation, familial history, payment methods, interactions with accompanying individuals, childhood caretakers, early negative experiences, length of hospitalization, tobacco and alcohol use, age, BPRS, and CRP level (P < 0.05; Table 2).
Table 2 Results of single factor analysis of violent behavior in hospitalized patients with severe mental disorders in the modeling module (n = 308), n (%).
Item
Violence (n = 232)
No violence (n = 76)
χ2/Z/t
P value
Diagnose
42.83
< 0.01
Schizophrenia
135 (58.19)
43 (56.58)
Bipolar affective disorder
24 (10.34)
18 (23.68)
Paranoid disorder
39 (16.81)
3 (3.96)
Schizoaffective disorder
34 (14.66)
4 (5.26)
Mental disorders caused by epilepsy
0 (0.00)
6 (7.89)
Mental retardation with mental disorder
0 (0.00)
2 (2.63)
Educational background
12.95
< 0.01
Primary school and below
50 (21.55)
31 (40.79)
Middle school/high school/vocational high school/vocational school
Logistic regression analysis of violent behavior in patients with SMDs in the modeling module
With the occurrence of violent behavior among inpatients with SMDs serving as the independent variable, values were allotted to various categorical variables (Table 3). Elements with P < 0.05 were considered significant in the univariate examination. Five variables were chosen as dependent factors for the logistic regression analysis. The findings indicated a correlation between level of education [β = -3.79, odds ratio (OR) = 0.02], cigarette smoking (β = 4.50, OR = 0.01), length of hospitalization (β = 0.49, OR = 4.77), age (β = -0.11, OR = 0.90), BPRS score (β = 0.17, OR = 1.19), and CRP level (β = 1.95, OR = 7.06). The study identified several independent predictors associated with aggressive actions in patients with SMDs (P < 0.05). Among the factors, level of education and age served as protective elements, whereas cigarette smoking, duration of hospital stay, BPRS score, and CRP level were identified as risk factors. Hospitalized patients with SMDs who were older, had higher levels of education, were non-smokers, were admitted during daytime hours, and had a lower BPRS score and CRP level demonstrated a reduced risk of displaying violent behavior. Additional details are included in Table 4.
Table 3 Assessment of violent behavior in severe mental disorder patients.
Construction of a nomogram model for predicting the risk of violent behavior in hospitalized patients with SMDs
A nomogram for predicting the risk of violent behavior among inpatients with SMDs was calculated as follows: Z = -12.21 - 3.79 × education + 4.50 × cigarette smoking + 0.49 × duration of hospitalization - 0.11 × age + 0.17 × BPRS score + 1.95 × CRP level. Figure 1 illustrates the use of R software to create a nomogram for visualization purposes.
Figure 1 Prediction line chart of violent behavior in hospitalized patients with severe mental disorders.
CRP: C-reactive protein; BPRS: Brief Psychiatric Rating Scale.
Validation of a prediction model for violent behavior in hospitalized patients with SMDs
distinguishing ability: An ROC curve (AUC) was generated using the forecasted variables derived from the model as the test parameters, and designating the incidence of aggressive acts in patients with SMDs during the hospital stay as the state parameter. The area under the ROC curve of the modeling group was 0.94 with a 95% confidence interval of 0.91-0.98. The AUC of the validation group was 0.93 with a 95% confidence interval of 0.86-1.00, demonstrating the effective performance of the forecasting model (Figure 2A).
Figure 2 Composite figure for efficacy evaluation of the violent behavior prediction model in hospitalized patients with severe mental disorders.
A: Receiver operating characteristic curves of the modeling and verification groups with respect to violent behavior in hospitalized patients with severe mental disorders (SMDs); B: H-L test of the predictive model of violent behavior in hospitalized patients with SMDs; C: Decision curve analysis curve of the prediction model of violent behavior in hospitalized patients with SMDs.
Calibration ability: The H-L test was utilized to assess the calibration capability of the risk prediction model for aggressive conduct in hospitalized patients with SMDs. The findings suggested that when forecasting the aggressive behavior of inpatients with SMDs, the estimated likelihood of the event was statistically indistinguishable from the real likelihood (χ2 = -7.06, P = 1.00). This system showcased exceptional accuracy (Figure 2B).
Clinical decision curve
DCA was used to ascertain if applying the prediction model in clinical settings offers more advantages than drawbacks. As illustrated in Figure 2C, significant clinical utility occurred when the likelihood was within 0.02-1.00.
DISCUSSION
A total of 440 inpatients diagnosed with SMDs were surveyed in the current study, which revealed a violent behavior incidence rate of 27.30%. This finding is consistent with the studies by Large and Nielssen[19] and Wang et al[20], but inconsistent with the report by Chen et al[21] and Zhang and Hu[22]. Various elements could account for these differences, as follows: (1) Each study had distinct inclusion and exclusion criteria. Chen et al[19] studied patients with bipolar disorder and mental illness in hospital, yet the current study was focused on examining hospitalized patients; (2) Different assessment instruments were used. In contrast to previous research, the current study utilized the 18-item BPRS. In addition, the other two studies used different scales or multiple scales to evaluate patients’ psychiatric symptoms, which may have contributed to the differing results; and (3) Currently, there is no unified definition for violent behavior in patients with mental disorders[23]. The severity of violent behavior is categorized into six levels: (1) No behavior meeting any of levels 1-5 (level 0); (2) Verbal threats or shouting without physical aggression (level 1); (3) Physical aggression that can be stopped through persuasion (level 2); (4) Obvious physical aggression that persists despite persuasion (level 3); (5) Persistent physical aggression that remains uncontrolled after counseling (level 4); and (6) Any violent act against individuals, including arson or explosions (level 5). In this study, hospitalized patients with SMDs were classified as non-violent if their follow-up risk assessment in 2019 showed levels 0-2, and as violent if levels 3-5[24]. Patients exhibiting violent behavior were placed under protective restraint with agreement secured from family members or accompanying staff. Other studies used different definitions of violent behavior.
Aggressive actions in patients result from a multifaceted combination of social, psychological, and environmental influences[25]. Recognizing these risk factors is vital for forecasting and averting aggression, as well as handling hospitalized patients with SMDs. The current study showed that factors, like level of education, age, cigarette smoking habit, and length of hospital stay independently impact violent actions in individuals with SMDs. Notably, advanced education and older age were shown to serve as protective factors, whereas younger age, cigarette smoking, hospital admission during the night, and a lower level of education were correlated with increased risk of violence. This investigation aligned with the findings from research carried out by Newton et al[26], Witt et al[27], and Fazel et al[28] for the following reasons: (1) Younger patients with SMDs often demonstrate increased physical energy yet encounter greater psychological pressure due to scholarly, career, financial, and familial obligations. Due to diminished impulse control and increased emotional response, younger patients with SMDs might be more prone to aggressive behavior[29]. Moreover, the initial phases of mental disorders pose a significant risk for aggressive actions, thereby heightening vulnerability among younger patients[30]; (2) Patients with advanced education frequently exhibit a greater grasp of their medical status, utilize healthier methods for coping, and follow treatment plans more diligently, which leads to decreased aggressive behaviors. In contrast, patients with less education might turn to hostility when faced with difficult situations; (3) The timing of hospitalization was shown to be a crucial factor because patients admitted during the nighttime shift frequently exhibit acute symptoms. Reduced staff availability during the nighttime hours can postpone timely interventions, increasing the risk of aggressive episodes; and (4) Tobacco consumption is associated with reduced effectiveness of antipsychotic drugs and decreased drug blood levels[31], which intensifies psychopathologic symptoms[32]. The influence of nicotine on serotonin concentrations in the central nervous system could potentially heighten negative feelings and aggression[33]. Moreover, elevated scores on the Psychoticism Rating Scale have been shown to have a separate link to aggressive actions, which is consistent with the results presented by Li et al[34] and Ling et al[35]. Symptoms measured by the BPRS scale, including hostility, hallucinations, and behavioral disruptions, have a significant correlation with aggression[36-38]. Emotional regulation is impacted by anxiety, depression, and emotional blunting, which can lead to increased violent behaviors[39,40]. The CRP level served as an additional indicator of aggression, confirming findings by Sun et al[41] and Joseph et al[42]. As a core biomarker of systemic inflammatory responses, elevated CRP levels are associated with an increased risk of violent behavior in patients with SMDs. This association stems from CRP disrupting brain function through multiple mechanisms that impair emotional regulation and impulse control. Acting as a “signaling molecule” in inflammatory activation, CRP interacts with neurotransmitters to indirectly reflect and exacerbate this pathological process, directly impairing cognitive functions[43], which may further increase the likelihood of aggressive behavior.
This study utilized R software to create a logistic regression model and design a predictive nomogram. The training and validation groups exhibited excellent sensitivity and specificity, achieving an AUC value > 0.90, which demonstrates outstanding forecasting capabilities. These findings indicate that the model is capable of accurately pinpointing inpatients who are at elevated risk for violent conduct, offering critical support to mental health specialists. The dependability of the model was further evaluated using calibration curves and DCA, affirming the robust clinical applicability. The results demonstrated the usefulness of the model in routine clinical practice for risk assessment and intervention strategy formulation. The primary risk factors associated with inpatient violence included young age, a lower level of education, cigarette smoking, a higher BPRS score, an elevated CRP level, and admission during nighttime hours. The constructed forecasting model facilitated the early detection of patients with SMDs at high risk of violent behavior, thereby enabling focused measures to prevent violent occurrences, improve patient care, and maximize the use of healthcare resources.
CONCLUSION
The current study focused on inpatients with SMDs in one hospital. Corollary studies should evaluate issues in psychiatric clinical settings using standardized violence evaluation instruments to improve clinical decisions. Furthermore, in future research conducting multicenter research with larger and varied sample sizes is essential to improve the accuracy and applicability of the results. These initiatives will enable the creation of clinically pertinent models for extensive adoption within psychiatric specialty hospitals.
Footnotes
Provenance and peer review: Unsolicited article; Externally peer reviewed.
Peer-review model: Single blind
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific Quality: Grade B, Grade C
Novelty: Grade B, Grade C
Creativity or Innovation: Grade B, Grade C
Scientific Significance: Grade B, Grade C
P-Reviewer: Wan HJ, Chief Nurse, Professor, Research Fellow, China; Xu H, PhD, Lecturer, China S-Editor: Hu XY L-Editor: A P-Editor: Zhao S
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