Establishment of a predictive model for violent behavior among outpatient patients in psychiatric hospitals

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Methods Patients who visited the Outpatient Department of Wutaishan Hospital in Yangzhou, Jiangsu Province from February to July 2024 were selected as investigation objects. By collecting data, the independent influencing factors of patients’ violent behavior were screened. A multi-factor Logistic regression model was established with R software and a column graph was plotted. The model was evaluated by receiver operating characteristic (ROC), calibration and clinical decision curves (DCA), and validated by the validation group. Results Among 430 outpatient patients with mental illness, 114 had violent behavior, with an incidence rate of 26.5%. Statistical significance was shown in many aspects of outpatients with mental illness (all P < 0.05): diagnosis, education, occupation, marriage, residence, family history, payment method, relationship with patients, psychological capital, adverse childhood experience, time of visit, smoking, drinking, age, number of visits, and psychotic symptoms (the Brief Psychiatric Rating Scale, BPRS). Educational background (odds ratio (OR) = 0.35), marriage (OR = 0.20), BPRS (OR = 1.10), visit time (OR = 4.83) and smoking (OR = 3.37) were independent risk factors for the violence of outpatients with mental illness. Among them, educational background and marital status were protective factors. Modeling and verification groups had an area under ROC curve (AUC) of 0.93 and 0.96, respectively. The calibration curve showed a good calibration degree of the model. The clinical decision curve demonstrated the good clinical effectiveness of the model. Conclusion The established risk prediction model for the violent behavior of outpatients with mental illness has good predictive efficacy, which is beneficial for clinical mental health personnel to early detect high-risk groups of violent behavior. Mental Illness Acts of Violence Influencing Factors Prediction Model Nomograph Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mental illness is a general term for a class of diseases[ 1 ], including schizophrenia, bipolar disorder, depression, etc. A majority of patients are prone to violent and aggressive behavior towards others, themselves and things around them due to emotional instability, which has a high risk. An act of violence[ 2 ] is a serious act that intentionally uses force or power to threaten or commit an act of aggression against oneself, others and a specific group or community, and threatens others’ safety and public order. Violence in the outpatient setting of psychiatric hospitals is a common problem[ 3 ] and often causes unpredictable consequences. Therefore, psychiatric outpatient nurses need to identify high-risk patients early to prevent violence and deal with it when it occurs, to reduce the negative impact of violence[ 4 ]. Violent behavior may be random and outside of nurses’ control[ 5 ]. Nevertheless, it is necessary for nurses to realize risk factors that make patients violent, like patients’ mental state and experiences of hallucinations or delusions that make patients feel threatened, fearful and hence more likely to become aggressive[ 6 ]. As noted by the American Association of Colleges of Nursing[ 7 ], can workplace violence be prevented ‘from a patient perspective’? Many foreign researchers have reported on workplace violence and its effects in literature[8.9] and found that patient violence has varied causes, but little research has specifically identified violence in psychiatric outpatients[ 10 ]. A systematic review study involving workplace violence in psychiatric departments in China[ 11 ] noticed that relevant factors encompassed patients, nursing, and social and environmental factors, but further research was not conducted. Who is ultimately responsible for the consequences of violent acts committed by people with mental illness? Hospitals take some steps to lower the risk of patients’ aggression. However, nurses on duty alone need to use careful assessment skills in the presence of a patient with a mental illness, while paying special attention to verbal and non-verbal cues suggesting possible violence. It is also important to ensure safety by providing technology that can identify violence at an early stage and take preventive measures accordingly. This study was aimed at analyzing the independent influencing factors for the occurrence of violence in outpatients with mental illness and building a predictive model. The purpose was to provide clinical mental health professionals with the early identification of high-risk patients and proper intervention measures based on their wishes, to maximize the safety of treatment. Methods and materials Survey respondents Patients with mental illness who visited the Outpatient Department of Wutaishan Hospital in Yangzhou, Jiangsu Province from February to July 2024 were chosen as investigation objects. Inclusion criteria: 1.Patients complied with the diagnostic criteria of the International Classification of Diseases, 10th revised edition (ICD-10)[ 12 ]; 2.With informed consent, patients themselves or their family members voluntarily participated in this study. Exclusion criteria: 1.Patients were accompanied by dementia, mental retardation, substance abuse or organic mental disorders; 2.Patients were accompanied by serious physical disease or other complications; 3.Those with cognitive or language impairment could not complete the scale; 4.Withdrawal of informed consent. The calculation method was determined based on the sample size of Logistic regression analysis[ 13 ]. Combined with literature reports, 58.35% of patients with mental illness showed violent behavior. Considering 10% invalid questionnaires, the minimum sample size required for the present study was calculated as follows: 18 × 5 × (1 + 10%) ÷ 58.35% = 170 cases. The final sample size included in the current study was 430 cases, which thus met the requirement of the minimum sample size. According to the ratio of 7:3, modeling and verification groups had 301 and 129 cases, respectively. This study gained the approval of the Ethics Committee of Wutaishan Hospital of Yangzhou, Jiangsu Province (ethics number: WTSLL2023025). All the subjects signed an informed consent form. General information questionnaire With reference to related literature, the general data table designed by the researchers included socio-demographic data such as gender, age, education, place of residence, educational level, marital status, time to see a doctor and family history. Brief Psychiatric Rating Scale The Brief Psychiatric Rating Scale (BPRS)[14.15] is used for assessing the severity of psychiatric symptoms in patients with mental illness. As the most widely used tool to evaluate the mental symptoms of psychiatric patients, BPRS is divided into 18 and 24 versions. The 18-item BPRS used in this study is classified into five types of factors, including the assessment of two negative symptoms: anxiety, depression and lack of vitality, and three positive symptoms: thinking disorders, activation and hostile suspicion. A 7-level score is adopted, with 1 point = no symptoms and 7 points = extremely severe. The BPRS results can be analyzed according to individual, score and total scores. The total score mirrors the severity of the disease, and the factor score reflects the clinical characteristics of mental patients’ symptoms. The total score is 18 to 126, and more than 35 is classified as abnormal. When the score is higher, the symptoms of patients will be more serious. The scale consists of 18 items, with Cronbach’s coefficient ranging between 0.787 and 0.97. Survey method From February 2024 to July, a questionnaire survey was conducted on the current situation and related influencing factors of violent behavior in outpatients with mental illness. Quality control In this study, a questionnaire survey was adopted to collect data, and a one-to-one questionnaire survey was conducted. Researchers who received unified training explained the study’s purpose and significance to patients. Patients were allowed to make choices under the premise of full understanding. Questionnaires were collected on the spot. During the process, the researchers checked the gaps and omissions in questionnaire filling on the scene, found problems and made up for them timely, coded and marked the questionnaire to ensure that the measurement was not repeated and the quality of recovery was guaranteed. In this study, 430 questionnaires were complete and judged to be valid. Statistical methods The data were statistically analyzed using Statistical Package for the Social Sciences (SPSS) 25.0 statistical software. The statistics of normal and skew distributions were expressed by x ± s and median (quartile), respectively. The frequency and composition ratio were described by categorical variables. Measurement data subject and not subject to normal distribution were compared by t and rank sum tests, respectively. The comparison between categorical variables was made by the x 2 test. Multi-factor logistic stepwise regression analysis was used for screening out independent risk factors affecting patients’ suicidal ideation. R4.1.2 software was utilized for building a risk prediction model and drawing a column graph to visualize the Logistic regression model. The predictive ability of the model was assessed by the ROC curve, and the model was internally verified by the Bootstrap verification method. P < 0.05 was considered to show statistical significance. Results General information on research objects Among 430 outpatients with mental illness, 114 (26.5%) had violent behavior, including 87 (28.9%) and 27 (21.0%) in modeling and verification groups, respectively. No statistically significant difference was observed in the incidence of violent behavior between both groups (all P > 0.05). Details are shown in Table 1 . Table 1 General information of research objects Item Sort Statistical value Violent behavior No 316(73.5%) a Yes 114(26.5%) a Gender Man 216(50.2%) a Woman 214(49.8%) a Diagnosis Schizophrenia 250(58.1%) a Bipolar disorder 64(14.9%) a Depression 9(2.1%) a Other mental disorders 107(24.9%) a Education Primary and below 118(27.4%) a Junior high school/high school/vocational high school/technical secondary school 232(54.0%) a College or above 80(18.6%) a Occupation None/student 296(68.8%) a Employee/worker/individual 81(18.8%) a retiree 53(12.3%) a Matrimony Unmarried/divorced/widowed 267(62.1%) a Married 163(37.9%) a Place of residence Village 266(61.9%) a Town 37(8.6%) a Downtown 127(29.5%) a Family history No 262(60.9%) a Yes 168(39.1%) a Payment method Self-financing 163(37.9%) a Health insurance/retirement 267(62.1%) a Escort relationship Husband/wife 106(24.7%) a Parents/children 202(47.0%) a Other relatives 48(11.2%) a Police/Cadres 74(17.2%) a Psychological capital Parents 380(88.4%) a Other 50(11.6%) a Adverse childhood experience No 374(87.0%) a Yes 56(13.0%) a Visit time Day shift 270(62.8%) a Three shifts 160(37.2%) a Smoking No 363(84.4%) a Yes 67(15.6%) a Tipple No 387(90.0%) a Yes 43(10.0%) a Age 45.59 ± 0.81 b Number of visits 2.0(1.0,7.0) c BPRS 44.0(35.0,58.0) c a: Frequency and composition ratio; b: Mean ± standard deviation; c: Median (quartile) Univariate analysis of violence among outpatients with mental illness The univariate analysis results showed that statistical significance was detected in the diagnosis, education, occupation, marriage, residence, family history, payment method, relationship with consultation staff, psychological capital, adverse childhood experience, time of consultation, smoking, drinking, age, frequency of consultation and BPRS of outpatient patients with mental illness (all P < 0.05). Details are shown in Table 2 . Table 2 Univariate analysis results of violence among outpatients with mental illness Item Statistical value P- value Diagnosis 54.79 a ˂ 0.01 Education 31.02 a ˂ 0.01 Occupation 23.53 a ˂ 0.01 Matrimony 70.23 a ˂ 0.01 Place of Residence 56.81 a ˂ 0.01 Family history 99.12 a ˂ 0.01 Payment method 130.80 a ˂ 0.01 Escort relationship 68.15 a ˂ 0.01 Psychological capital 32.51 a ˂ 0.01 Adverse childhood experience 27.50 a ˂ 0.01 Visit time 152.20 a ˂ 0.01 Smoking 77.57 a ˂ 0.01 Tipple 80.26 a ˂ 0.01 Age 57.61 b ˂ 0.01 Number of visits 7.52 c ˂ 0.01 BPRS 154.76 c ˂ 0.01 a: Chi-square test; b: T test; c: Rank sum test Multivariate Logistic regression analysis of influencing factors of violent behavior in outpatients with mental illness Multiple categorical variables were assigned with the occurrence of violence in outpatients with mental illness as the independent variable. Variables with P < 0.05 in univariate analysis were used as dependent variables for multivariate Logistic regression analysis. The multivariate analysis results showed that education (odds ratio (OR) = 0.35), marriage (OR = 0.20), BPRS (OR = 1.10), visit time (OR = 4.83) and smoking (OR = 3.37) were independent risk factors for violence in outpatients with mental illness. Among them, education and marital status were protective factors. Details are demonstrated in Table 3 and Table 4 . Table 3 Variable assignment Variable Assignment specification Violent behavior No = 1, Yes = 2 Education Primary school and below = 1, junior high school/high school/vocational high school/technical secondary school = 2, junior college and above = 3 Matrimony Unmarried/divorced/widowed = 1, married = 2 Visit time Day shift = 1, three shifts = 2 smoking No = 1, Yes = 2 Table 4 Multivariate analysis of violent behavior in outpatients with mental illness Independent variable Regression coefficient Standard error P-value OR-value OR-value 95% confidence interval (CI)] Constant -5.94 2.27 0.01 Education -1.04 0.38 0.01 0.35 [0.17 ~ 0.75] Matrimony -1.59 0.61 0.01 0.20 [0.06 ~ 0.67] BPRS 0.10 0.02 ˂ 0.01 1.10 [1.07 ~ 1.14] Visit time 1.58 0.47 ˂ 0.01 4.83 [1.94 ~ 12.05] Smoking 1.22 0.60 0.04 3.37 [1.04 ~ 10.98] Construction of a nomogram prediction model for the risk of violent behavior in outpatients with mental illness Based on the Logistic regression model: Z = -5.94-1.04 × educational background − 1.59 × marriage + 0.10 × BPRS score + 1.58 × medical visit time + 1.22 × smoking, R software was used for constructing a nomogram for visualization, as illustrated in Fig. 1 . The specific situation of every risk factor in the column diagram corresponded to the corresponding score value. The score values of the five indicators in the model were added to get the total score, where the vertical line was drawn downward. The value that corresponded to the intersection point of the vertical line and the coordinate of “probability of violent behavior” was the risk of violent behavior of patients with outpatient mental illness. Validation of risk prediction models for violent behavior in outpatients with mental illness Differentiating capability According to the predictive variables obtained by the model as test variables, the occurrence of violent behavior in outpatients with mental illness was used as the state variable for drawing the ROC curve. The modeling group had an AUC value of 0.93, a Jorden index of 0.78, and a sensitivity and specificity of 0.80 and 0.98, respectively. The AUC value of the prediction model in the verification group was 0.96; the Jorden index was 0.82 when the optimal cut-off value was taken; sensitivity and specificity were 0.96 and 0.86, respectively. The results showed the efficiency of the prediction model. Details are presented in Figs. 2 . Calibration capability To evaluate the calibration capability of the risk prediction model for the violent behavior of outpatients with mental illness, the Hosmer-Lemeshow (H-L) test was used. The results revealed that x 2 = -23.97, p = 1.00. This indicated that no statistical difference was noted between the predicted and actual probabilities of occurrence of violence in outpatients with mental illness in this model, and this model has good calibration ability. Details are displayed in Fig. 3 . Clinical decision curve To assess the clinical utility of a predictive model for the risk of violent behavior in outpatient psychiatric patients, a clinical decision curve analysis was performed to determine whether the benefits of using predictive models to inform clinical decisions outweigh the risks. As can be seen from Fig. 4 , high clinical effectiveness can be obtained when the probability falls between 0.10 and 0.98. Discussion Analysis of the current situation of violent behavior in outpatients with mental illness Among the 430 outpatient patients with mental illness in this survey, the incidence of violent behavior was 26.5%, which was in line with the results of Large and Wang et al.[16.17], but different from the results of Chen and Zhang et al.[18.19]. The possible reasons are as follows: (1) Differences exist in the research objects and different tolerance standards in different studies. The subjects of the present study are patients in the Outpatient Department of a psychiatric hospital. (2) Differences exist in the assessment scales used. The scale used in this study is the 18-item BPRS scale. (3) The criteria for the evaluation of violence are inconsistent. The criteria for the evaluation of outcome variables in this study are as follows: patients with mental illness who had violent behavior during a hospital visit and underwent protective restraint with the consent of family members or accompanying staff. Risk factors for the violent behavior of outpatients with mental illness The occurrence of patients’ violent behavior results from multiple factors such as social, psychological and environmental factors[ 20 ]. It is of great significance to get familiar with these risk factors for the prediction, early warning, intervention and management of aggressive behavior of outpatients with mental illness. Sociodemographic factors (education, marriage, smoking and time of visit) In this study, it was found that education, marriage, smoking and time of visit were independent influencing factors for violent behavior in outpatient psychiatric patients, while education and marriage were protective factors. Patients who saw a doctor on the day shift were married, did not smoke and had higher education were less likely to have violent behavior. This view was proved by the research of Newton, Katrina, Witt and other scholars[ 21 – 23 ]. The reason may be as follows: (1) Family and society may not change the requirements of married patients in the state of disease in all aspects. As a result, they will be faced with greater psychological pressure than patients in a normal state, which results in more emotions and increases the probability of violent behavior. (2) People with higher education receive good education. The scientific cognition of disease, the abandonment of feudalism and the proper handling of conflicts in real life are conducive to reducing the occurrence of violence. Patients with low education are more inclined to take aggressive measures when in trouble, which results in violent behavior. In addition, well-educated patients with mental illness may be better able to follow medical advice and have a more objective and scientific understanding of the disease, which helps to systematically and effectively treat the disease and reduce the occurrence of violent behavior. (3) When patients coming to the hospital on non-day shifts are more urgent and serious, fewer employees are on the post at this time and the disease control of patients is not rapid, such patients are more likely to produce violent behavior. (4) The effect of antipsychotic drugs taken by smoking patients is worse than that of non-smoking patients. The blood drug concentration in the body is reduced, and the drug effect is poor[ 24 ]. It has been found that smoking is strongly predictive of psychopathological symptoms, and such patients have more severe psychotic symptoms[ 25 ]. Additionally, nicotine produced by smoking enters the central nervous system, which may lead to abnormal levels of serotonin in the brain. In this case, individuals will show bad emotions and thus are more prone to violent behavior[ 26 ]. Psychotic symptoms In this study, it was shown that the score on the psychiatric scale is an independent factor influencing the occurrence of violent behavior in outpatients with mental illness. The higher the score is, the more likely violent behavior will occur. This is consistent with the research results of Li et al.[27.28], which indicate that clinical psychiatric symptoms are closely related to violent behavior. The reasons may be as follows: (1) Specific mental symptoms in the BPRS scale are associated with the occurrence of violent behavior[ 29 ], and patients with high scores of mental disorders tend to have violent behavior[ 30 ]. (2) Suspicion, hallucination, hostility factors and behavioral disorders in the BPRS scale are linked to the aggressive behavior of mental patients. The occurrence of aggressive behavior of mental patients may be caused by hostility, suspicion and hallucination symptoms[31.32]. (3) The scores of the two negative symptoms of anxiety, depression and lack of energy in the BPRS scale affect the development and regulation of emotions[ 33 ], and thus significantly influence the violent behavior of patients with mental disorders[ 34 ]. Ideal predictive effect of the violent behavior risk prediction model for outpatients with mental illness In this study, R software was used for building a Logistic regression model and drawing a column graph. The sensitivity and specificity of modeling and verification groups were high, and both groups had an AUC of greater than 0.90. This indicated that the model had an ideal prediction effect and assisted mental health professionals in early screening the high-risk groups of violence during outpatient visits. Meanwhile, calibration and clinical decision curves were used to evaluate the calibration degree and effectiveness of the model. The results showed that the model could be utilized in clinical diagnosis and treatment activities and had good clinical application value. Conclusion And Recommendations In summary, the characteristics of patients with high and low risks of violent behavior were explored in this study. The results showed that patients who were unmarried, had low education, smoked, had high BPRS scores, and saw the doctor during three shifts were more likely to produce violent behavior during psychiatric outpatient visits. Violent behavior prediction models can be used for screening and evaluating patients with potentially violent behavior. Personalized intervention measures can be implemented for different groups to more effectively decrease the occurrence and severity of violent behavior of patients in mental health care units, and raise medical efficiency. The research objects included in this study are only patients in the designated hospital. Future research should face up to many problems in clinical research in the domain of psychiatry, unify the definition of aggressive behavior, focus on strict and reasonable experimental design, and use standardized assessment tools for violent behavior to provide a basis for optimal clinical decision-making. In the meantime, a multi-center, multi-area large sample study was carried out, and constantly adjusted to make it more accurate and more clinical to be widely used in the outpatient departments of psychiatric hospitals. Declarations Acknowledgements The first authors want to thank all participants. Authors’ contributions Ting Wang: Investigation, Writing and editing, Funding Support; Ping Zhao: Methodology, Investigation. Jiaojiao Sun: Investigation and data analysis. Yaqin Zhao: Investigation, data analysis and Funding Support, Gao Lini and Nan Liu: Conceptualization and data analysis. Jia Li: Supervision, Conceptualization, Writing - review & editing. Lin Wang: Writing - review & editing, Funding Support. The author(s) read and approved the fnal manuscript. Funding This study were supported by the 2022 annual hospital project funding fund of Yangzhou Wutai Mountain Hospital in Jiangsu Province (Lin Wang WTS2022004), (Yaqin Zhao WTS2022008), (Ting Wang WTS2022021) and the 2023 Annual Medical Scientific Research Project of Yangzhou Municipal Health Commission (Yaqin Zhao 2023-4-4). Availability of data and materials 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. Ethics approval and consent to participate All participants signed the informed consent form before entering the study, and all procedures performed in this study involving human participants were in accordance with the Declaration of HelsinkiEthics approval and consent to. This study earned the approval of the Ethics Committee of the Yangzhou Wutai Mountain Hospital (WTSLL2023025). Consent for publication All participants provided informed consent, including consent to publish scientifc manuscripts based on collected data, in order to participate in the study. Competing interests The authors declare that they have no competing interests. Clinical trial number not applicable. Clinical trial number not applicable. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6166504","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435635560,"identity":"c4747f66-81e0-4f8b-a1af-a787e1e967d9","order_by":0,"name":"Ting Wang","email":"","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Wang","suffix":""},{"id":435635561,"identity":"bed9e5de-4a34-4491-a2c0-7c868d490ebe","order_by":1,"name":"Ping Zhao","email":"","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ping","middleName":"","lastName":"Zhao","suffix":""},{"id":435635562,"identity":"be87defb-364d-467c-b418-709b8dedcb03","order_by":2,"name":"Jiaojiao Sun","email":"","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jiaojiao","middleName":"","lastName":"Sun","suffix":""},{"id":435635563,"identity":"90fc08fc-3b18-4974-aa76-19816c117df5","order_by":3,"name":"Yaqin Zhao","email":"","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yaqin","middleName":"","lastName":"Zhao","suffix":""},{"id":435635564,"identity":"b05434f6-8dd7-4baa-85ed-b384f7009d3b","order_by":4,"name":"Lini Gao","email":"","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Lini","middleName":"","lastName":"Gao","suffix":""},{"id":435635565,"identity":"7e52fe49-d325-40cc-aae2-3e79f7a17194","order_by":5,"name":"Nan Liu","email":"","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Nan","middleName":"","lastName":"Liu","suffix":""},{"id":435635566,"identity":"ddcde822-c26a-4f9b-809f-d1f438cc8c8a","order_by":6,"name":"Jia Li","email":"","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":false,"prefix":"","firstName":"Jia","middleName":"","lastName":"Li","suffix":""},{"id":435635567,"identity":"5ce042a0-4cdf-4c44-b774-37a33fafd86c","order_by":7,"name":"Lin Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACNobDBw4kVLDZ8csfPkCcFj7GY4kHHpzhS5acwZZAnBY55jPGBx+2yDFuuMFjQKTD2A4YHEhsMGM2uN3z8cYbBjs53QZCWngOJBxI3JHGJ3nn7GbLOQzJxmYHCGmROHDgQOKZY8x8B3K3SfMwHEjcRlCL/MOGA4lt/xkbDuQ8I1ILw2GgsjY2xgk3ctiI1XKM4UDCGbZkyZ5jxpZzDIjwi3zD+c8ff4Cikr354Y03FXZyBLWgAAliowZZC6k6RsEoGAWjYEQAAI7TS09Qc3T7AAAAAElFTkSuQmCC","orcid":"","institution":"Yangzhou Wutaishan Hospital of Jiangsu Province, Teaching hospital of Yangzhou University","correspondingAuthor":true,"prefix":"","firstName":"Lin","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-03-06 03:08:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6166504/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6166504/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79673593,"identity":"f4505e88-32c0-42ff-858b-a28a259e047f","added_by":"auto","created_at":"2025-04-01 11:49:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":35563,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram of the risk prediction of violent behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the Logistic regression model: Z = -5.94-1.04 × educational background -1.59 × marriage + 0.10 × BPRS score + 1.58 × medical visit time + 1.22 × smoking, R software was used for constructing a nomogram for visualization.\u003c/p\u003e\n\u003cp\u003eA nomogram of the risk prediction of violent behavior\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6166504/v1/ee521395124132eb82d888c9.png"},{"id":79673595,"identity":"52e8abb3-2fff-41d3-812e-8a03f9484d12","added_by":"auto","created_at":"2025-04-01 11:49:32","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57793,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eROC curve of the risk of violent behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to the predictive variables obtained by the model as test variables, the occurrence of violent behavior in outpatients with mental illness was used as the state variable for drawing the ROC curve. The modeling group had an AUC value of 0.93, a Jorden index of 0.78, and a sensitivity and specificity of 0.80 and 0.98, respectively. The AUC value of the prediction model in the verification group was 0.96; the Jorden index was 0.82 when the optimal cut-off value was taken; sensitivity and specificity were 0.96 and 0.86, respectively. The results showed the efficiency of the prediction model.\u003c/p\u003e\n\u003cp\u003eROC curve of the risk of violent behavior\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6166504/v1/c6910d3528827dd14b59b93d.jpeg"},{"id":79673594,"identity":"30274fba-114a-4daf-81dd-0c311fc4d64f","added_by":"auto","created_at":"2025-04-01 11:49:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":50933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ecalibration curves of the nomogram for the risk of violent behavior\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the calibration capability of the risk prediction model for the violent behavior of outpatients with mental illness, the Hosmer-Lemeshow (H-L) test was used. The results revealed that \u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = -23.97, \u003cem\u003ep\u003c/em\u003e = 1.00. This indicated that no statistical difference was noted between the predicted and actual probabilities of occurrence of violence in outpatients with mental illness in this model, and this model has good calibration ability.\u003c/p\u003e\n\u003cp\u003ecalibration curves of the nomogram for the risk of violent behavior\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6166504/v1/c55e32df0251754772fb1229.png"},{"id":79673602,"identity":"ede47ad3-4529-4387-a47e-0385238050b5","added_by":"auto","created_at":"2025-04-01 11:49:32","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":156981,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDecision curve analysis for the detection of patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the clinical utility of a predictive model for the risk of violent behavior in outpatient psychiatric patients, a clinical decision curve analysis was performed to determine whether the benefits of using predictive models to inform clinical decisions outweigh the risks. High clinical effectiveness can be obtained when the probability falls between 0.10 and 0.98.\u003c/p\u003e\n\u003cp\u003eDecision curve analysis for the detection of patients\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6166504/v1/67b677749e4e0f53e3529174.jpeg"},{"id":83776671,"identity":"9f37c562-7070-4e95-a173-bb66dbf61406","added_by":"auto","created_at":"2025-06-02 14:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1543042,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6166504/v1/cf56079d-bd8c-474d-aefb-bd72e52fd45d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Establishment of a predictive model for violent behavior among outpatient patients in psychiatric hospitals","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMental illness is a general term for a class of diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], including schizophrenia, bipolar disorder, depression, etc. A majority of patients are prone to violent and aggressive behavior towards others, themselves and things around them due to emotional instability, which has a high risk. An act of violence[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] is a serious act that intentionally uses force or power to threaten or commit an act of aggression against oneself, others and a specific group or community, and threatens others\u0026rsquo; safety and public order. Violence in the outpatient setting of psychiatric hospitals is a common problem[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and often causes unpredictable consequences. Therefore, psychiatric outpatient nurses need to identify high-risk patients early to prevent violence and deal with it when it occurs, to reduce the negative impact of violence[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eViolent behavior may be random and outside of nurses\u0026rsquo; control[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Nevertheless, it is necessary for nurses to realize risk factors that make patients violent, like patients\u0026rsquo; mental state and experiences of hallucinations or delusions that make patients feel threatened, fearful and hence more likely to become aggressive[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As noted by the American Association of Colleges of Nursing[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], can workplace violence be prevented \u0026lsquo;from a patient perspective\u0026rsquo;? Many foreign researchers have reported on workplace violence and its effects in literature[8.9] and found that patient violence has varied causes, but little research has specifically identified violence in psychiatric outpatients[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A systematic review study involving workplace violence in psychiatric departments in China[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] noticed that relevant factors encompassed patients, nursing, and social and environmental factors, but further research was not conducted. Who is ultimately responsible for the consequences of violent acts committed by people with mental illness? Hospitals take some steps to lower the risk of patients\u0026rsquo; aggression. However, nurses on duty alone need to use careful assessment skills in the presence of a patient with a mental illness, while paying special attention to verbal and non-verbal cues suggesting possible violence. It is also important to ensure safety by providing technology that can identify violence at an early stage and take preventive measures accordingly. This study was aimed at analyzing the independent influencing factors for the occurrence of violence in outpatients with mental illness and building a predictive model. The purpose was to provide clinical mental health professionals with the early identification of high-risk patients and proper intervention measures based on their wishes, to maximize the safety of treatment.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSurvey respondents\u003c/h2\u003e \u003cp\u003ePatients with mental illness who visited the Outpatient Department of Wutaishan Hospital in Yangzhou, Jiangsu Province from February to July 2024 were chosen as investigation objects. Inclusion criteria: 1.Patients complied with the diagnostic criteria of the International Classification of Diseases, 10th revised edition (ICD-10)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]; 2.With informed consent, patients themselves or their family members voluntarily participated in this study. Exclusion criteria: 1.Patients were accompanied by dementia, mental retardation, substance abuse or organic mental disorders; 2.Patients were accompanied by serious physical disease or other complications; 3.Those with cognitive or language impairment could not complete the scale; 4.Withdrawal of informed consent. The calculation method was determined based on the sample size of Logistic regression analysis[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Combined with literature reports, 58.35% of patients with mental illness showed violent behavior. Considering 10% invalid questionnaires, the minimum sample size required for the present study was calculated as follows: 18 \u0026times; 5 \u0026times; (1\u0026thinsp;+\u0026thinsp;10%)\u0026thinsp;\u0026divide;\u0026thinsp;58.35% = 170 cases. The final sample size included in the current study was 430 cases, which thus met the requirement of the minimum sample size. According to the ratio of 7:3, modeling and verification groups had 301 and 129 cases, respectively.\u003c/p\u003e \u003cp\u003e This study gained the approval of the Ethics Committee of Wutaishan Hospital of Yangzhou, Jiangsu Province (ethics number: WTSLL2023025). All the subjects signed an informed consent form.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeneral information questionnaire\u003c/h3\u003e\n\u003cp\u003eWith reference to related literature, the general data table designed by the researchers included socio-demographic data such as gender, age, education, place of residence, educational level, marital status, time to see a doctor and family history.\u003c/p\u003e\n\u003ch3\u003eBrief Psychiatric Rating Scale\u003c/h3\u003e\n\u003cp\u003eThe Brief Psychiatric Rating Scale (BPRS)[14.15] is used for assessing the severity of psychiatric symptoms in patients with mental illness. As the most widely used tool to evaluate the mental symptoms of psychiatric patients, BPRS is divided into 18 and 24 versions. The 18-item BPRS used in this study is classified into five types of factors, including the assessment of two negative symptoms: anxiety, depression and lack of vitality, and three positive symptoms: thinking disorders, activation and hostile suspicion. A 7-level score is adopted, with 1 point\u0026thinsp;=\u0026thinsp;no symptoms and 7 points\u0026thinsp;=\u0026thinsp;extremely severe. The BPRS results can be analyzed according to individual, score and total scores. The total score mirrors the severity of the disease, and the factor score reflects the clinical characteristics of mental patients\u0026rsquo; symptoms. The total score is 18 to 126, and more than 35 is classified as abnormal. When the score is higher, the symptoms of patients will be more serious. The scale consists of 18 items, with Cronbach\u0026rsquo;s coefficient ranging between 0.787 and 0.97.\u003c/p\u003e\n\u003ch3\u003eSurvey method\u003c/h3\u003e\n\u003cp\u003eFrom February 2024 to July, a questionnaire survey was conducted on the current situation and related influencing factors of violent behavior in outpatients with mental illness.\u003c/p\u003e\n\u003ch3\u003eQuality control\u003c/h3\u003e\n\u003cp\u003eIn this study, a questionnaire survey was adopted to collect data, and a one-to-one questionnaire survey was conducted. Researchers who received unified training explained the study\u0026rsquo;s purpose and significance to patients. Patients were allowed to make choices under the premise of full understanding. Questionnaires were collected on the spot.\u003c/p\u003e \u003cp\u003eDuring the process, the researchers checked the gaps and omissions in questionnaire filling on the scene, found problems and made up for them timely, coded and marked the questionnaire to ensure that the measurement was not repeated and the quality of recovery was guaranteed. In this study, 430 questionnaires were complete and judged to be valid.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical methods\u003c/h2\u003e \u003cp\u003eThe data were statistically analyzed using Statistical Package for the Social Sciences (SPSS) 25.0 statistical software. The statistics of normal and skew distributions were expressed by x\u0026thinsp;\u0026plusmn;\u0026thinsp;s and median (quartile), respectively. The frequency and composition ratio were described by categorical variables. Measurement data subject and not subject to normal distribution were compared by t and rank sum tests, respectively. The comparison between categorical variables was made by the \u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e test. Multi-factor logistic stepwise regression analysis was used for screening out independent risk factors affecting patients\u0026rsquo; suicidal ideation. R4.1.2 software was utilized for building a risk prediction model and drawing a column graph to visualize the Logistic regression model. The predictive ability of the model was assessed by the ROC curve, and the model was internally verified by the Bootstrap verification method. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered to show statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGeneral information on research objects\u003c/h2\u003e \u003cp\u003eAmong 430 outpatients with mental illness, 114 (26.5%) had violent behavior, including 87 (28.9%) and 27 (21.0%) in modeling and verification groups, respectively. No statistically significant difference was observed in the incidence of violent behavior between both groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeneral information of research objects\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStatistical value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eViolent behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e316(73.5%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e114(26.5%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e216(50.2%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWoman\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e214(49.8%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSchizophrenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e250(58.1%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBipolar disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64(14.9%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(2.1%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther mental disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e107(24.9%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118(27.4%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school/high school/vocational high school/technical secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e232(54.0%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(18.6%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNone/student\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e296(68.8%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee/worker/individual\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81(18.8%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eretiree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53(12.3%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMatrimony\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried/divorced/widowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267(62.1%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163(37.9%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVillage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e266(61.9%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(8.6%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDowntown\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127(29.5%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e262(60.9%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e168(39.1%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePayment method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-financing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e163(37.9%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHealth insurance/retirement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e267(62.1%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEscort relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHusband/wife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106(24.7%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParents/children\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e202(47.0%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther relatives\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(11.2%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePolice/Cadres\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74(17.2%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePsychological capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eParents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e380(88.4%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50(11.6%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAdverse childhood experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e374(87.0%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e56(13.0%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVisit time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay shift\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e270(62.8%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThree shifts\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160(37.2%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e363(84.4%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67(15.6%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTipple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e387(90.0%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(10.0%)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45.59\u0026thinsp;\u0026plusmn;\u0026thinsp;0.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.0(1.0,7.0)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44.0(35.0,58.0)\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ea: Frequency and composition ratio; b: Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation; c: Median (quartile)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eUnivariate analysis of violence among outpatients with mental illness\u003c/h2\u003e \u003cp\u003eThe univariate analysis results showed that statistical significance was detected in the diagnosis, education, occupation, marriage, residence, family history, payment method, relationship with consultation staff, psychological capital, adverse childhood experience, time of consultation, smoking, drinking, age, frequency of consultation and BPRS of outpatient patients with mental illness (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Details are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eUnivariate analysis results of violence among outpatients with mental illness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eItem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatistical value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP-\u003c/em\u003evalue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e54.79\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31.02\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.53\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrimony\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of Residence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.81\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily history\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99.12\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePayment method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e130.80\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEscort relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e68.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32.51\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdverse childhood experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e152.20\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.57\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTipple\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.26\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57.61\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of visits\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.52\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154.76\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003ea: Chi-square test; b: T test; c: Rank sum test\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMultivariate Logistic regression analysis of influencing factors of violent behavior in outpatients with mental illness\u003c/h2\u003e \u003cp\u003eMultiple categorical variables were assigned with the occurrence of violence in outpatients with mental illness as the independent variable. Variables with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in univariate analysis were used as dependent variables for multivariate Logistic regression analysis. The multivariate analysis results showed that education (odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.35), marriage (OR\u0026thinsp;=\u0026thinsp;0.20), BPRS (OR\u0026thinsp;=\u0026thinsp;1.10), visit time (OR\u0026thinsp;=\u0026thinsp;4.83) and smoking (OR\u0026thinsp;=\u0026thinsp;3.37) were independent risk factors for violence in outpatients with mental illness. Among them, education and marital status were protective factors. Details are demonstrated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable assignment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAssignment specification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViolent behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;1, Yes\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary school and below =\u0026thinsp;1, junior high school/high school/vocational high school/technical secondary school\u0026thinsp;=\u0026thinsp;2, junior college and above =\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrimony\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnmarried/divorced/widowed\u0026thinsp;=\u0026thinsp;1, married\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDay shift\u0026thinsp;=\u0026thinsp;1, three shifts\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u0026thinsp;=\u0026thinsp;1, Yes\u0026thinsp;=\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate analysis of violent behavior in outpatients with mental illness\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegression coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOR-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOR-value 95% confidence interval (CI)]\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.17\u0026thinsp;~\u0026thinsp;0.75]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMatrimony\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[0.06\u0026thinsp;~\u0026thinsp;0.67]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBPRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.07\u0026thinsp;~\u0026thinsp;1.14]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVisit time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e˂ 0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.94\u0026thinsp;~\u0026thinsp;12.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e[1.04\u0026thinsp;~\u0026thinsp;10.98]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eConstruction of a nomogram prediction model for the risk of violent behavior in outpatients with mental illness\u003c/b\u003e \u003c/p\u003e \u003cp\u003eBased on the Logistic regression model: Z = -5.94-1.04 \u0026times; educational background \u0026minus;\u0026thinsp;1.59 \u0026times; marriage\u0026thinsp;+\u0026thinsp;0.10 \u0026times; BPRS score\u0026thinsp;+\u0026thinsp;1.58 \u0026times; medical visit time\u0026thinsp;+\u0026thinsp;1.22 \u0026times; smoking, R software was used for constructing a nomogram for visualization, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eThe specific situation of every risk factor in the column diagram corresponded to the corresponding score value. The score values of the five indicators in the model were added to get the total score, where the vertical line was drawn downward. The value that corresponded to the intersection point of the vertical line and the coordinate of \u0026ldquo;probability of violent behavior\u0026rdquo; was the risk of violent behavior of patients with outpatient mental illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eValidation of risk prediction models for violent behavior in outpatients with mental illness\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003eDifferentiating capability\u003c/h2\u003e \u003cp\u003eAccording to the predictive variables obtained by the model as test variables, the occurrence of violent behavior in outpatients with mental illness was used as the state variable for drawing the ROC curve. The modeling group had an AUC value of 0.93, a Jorden index of 0.78, and a sensitivity and specificity of 0.80 and 0.98, respectively. The AUC value of the prediction model in the verification group was 0.96; the Jorden index was 0.82 when the optimal cut-off value was taken; sensitivity and specificity were 0.96 and 0.86, respectively. The results showed the efficiency of the prediction model. Details are presented in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCalibration capability\u003c/h2\u003e \u003cp\u003eTo evaluate the calibration capability of the risk prediction model for the violent behavior of outpatients with mental illness, the Hosmer-Lemeshow (H-L) test was used. The results revealed that \u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e = -23.97, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1.00. This indicated that no statistical difference was noted between the predicted and actual probabilities of occurrence of violence in outpatients with mental illness in this model, and this model has good calibration ability. Details are displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eClinical decision curve\u003c/h2\u003e \u003cp\u003eTo assess the clinical utility of a predictive model for the risk of violent behavior in outpatient psychiatric patients, a clinical decision curve analysis was performed to determine whether the benefits of using predictive models to inform clinical decisions outweigh the risks. As can be seen from Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e, high clinical effectiveness can be obtained when the probability falls between 0.10 and 0.98.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis of the current situation of violent behavior in outpatients with mental illness\u003c/h2\u003e \u003cp\u003eAmong the 430 outpatient patients with mental illness in this survey, the incidence of violent behavior was 26.5%, which was in line with the results of Large and Wang et al.[16.17], but different from the results of Chen and Zhang et al.[18.19]. The possible reasons are as follows: (1) Differences exist in the research objects and different tolerance standards in different studies. The subjects of the present study are patients in the Outpatient Department of a psychiatric hospital. (2) Differences exist in the assessment scales used. The scale used in this study is the 18-item BPRS scale. (3) The criteria for the evaluation of violence are inconsistent. The criteria for the evaluation of outcome variables in this study are as follows: patients with mental illness who had violent behavior during a hospital visit and underwent protective restraint with the consent of family members or accompanying staff.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eRisk factors for the violent behavior of outpatients with mental illness\u003c/h2\u003e \u003cp\u003eThe occurrence of patients\u0026rsquo; violent behavior results from multiple factors such as social, psychological and environmental factors[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It is of great significance to get familiar with these risk factors for the prediction, early warning, intervention and management of aggressive behavior of outpatients with mental illness.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic factors (education, marriage, smoking and time of visit)\u003c/h2\u003e \u003cp\u003eIn this study, it was found that education, marriage, smoking and time of visit were independent influencing factors for violent behavior in outpatient psychiatric patients, while education and marriage were protective factors. Patients who saw a doctor on the day shift were married, did not smoke and had higher education were less likely to have violent behavior. This view was proved by the research of Newton, Katrina, Witt and other scholars[\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The reason may be as follows: (1) Family and society may not change the requirements of married patients in the state of disease in all aspects. As a result, they will be faced with greater psychological pressure than patients in a normal state, which results in more emotions and increases the probability of violent behavior. (2) People with higher education receive good education. The scientific cognition of disease, the abandonment of feudalism and the proper handling of conflicts in real life are conducive to reducing the occurrence of violence. Patients with low education are more inclined to take aggressive measures when in trouble, which results in violent behavior. In addition, well-educated patients with mental illness may be better able to follow medical advice and have a more objective and scientific understanding of the disease, which helps to systematically and effectively treat the disease and reduce the occurrence of violent behavior. (3) When patients coming to the hospital on non-day shifts are more urgent and serious, fewer employees are on the post at this time and the disease control of patients is not rapid, such patients are more likely to produce violent behavior. (4) The effect of antipsychotic drugs taken by smoking patients is worse than that of non-smoking patients. The blood drug concentration in the body is reduced, and the drug effect is poor[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. It has been found that smoking is strongly predictive of psychopathological symptoms, and such patients have more severe psychotic symptoms[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Additionally, nicotine produced by smoking enters the central nervous system, which may lead to abnormal levels of serotonin in the brain. In this case, individuals will show bad emotions and thus are more prone to violent behavior[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003ePsychotic symptoms\u003c/h2\u003e \u003cp\u003eIn this study, it was shown that the score on the psychiatric scale is an independent factor influencing the occurrence of violent behavior in outpatients with mental illness. The higher the score is, the more likely violent behavior will occur. This is consistent with the research results of Li et al.[27.28], which indicate that clinical psychiatric symptoms are closely related to violent behavior. The reasons may be as follows: (1) Specific mental symptoms in the BPRS scale are associated with the occurrence of violent behavior[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and patients with high scores of mental disorders tend to have violent behavior[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. (2) Suspicion, hallucination, hostility factors and behavioral disorders in the BPRS scale are linked to the aggressive behavior of mental patients. The occurrence of aggressive behavior of mental patients may be caused by hostility, suspicion and hallucination symptoms[31.32]. (3) The scores of the two negative symptoms of anxiety, depression and lack of energy in the BPRS scale affect the development and regulation of emotions[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], and thus significantly influence the violent behavior of patients with mental disorders[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIdeal predictive effect of the violent behavior risk prediction model for outpatients with mental illness\u003c/h2\u003e \u003cp\u003eIn this study, R software was used for building a Logistic regression model and drawing a column graph. The sensitivity and specificity of modeling and verification groups were high, and both groups had an AUC of greater than 0.90. This indicated that the model had an ideal prediction effect and assisted mental health professionals in early screening the high-risk groups of violence during outpatient visits. Meanwhile, calibration and clinical decision curves were used to evaluate the calibration degree and effectiveness of the model. The results showed that the model could be utilized in clinical diagnosis and treatment activities and had good clinical application value.\u003c/p\u003e "},{"header":"Conclusion And Recommendations","content":"\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003cp\u003eIn summary, the characteristics of patients with high and low risks of violent behavior were explored in this study. The results showed that patients who were unmarried, had low education, smoked, had high BPRS scores, and saw the doctor during three shifts were more likely to produce violent behavior during psychiatric outpatient visits. Violent behavior prediction models can be used for screening and evaluating patients with potentially violent behavior. Personalized intervention measures can be implemented for different groups to more effectively decrease the occurrence and severity of violent behavior of patients in mental health care units, and raise medical efficiency.\u003c/p\u003e \u003cp\u003eThe research objects included in this study are only patients in the designated hospital. Future research should face up to many problems in clinical research in the domain of psychiatry, unify the definition of aggressive behavior, focus on strict and reasonable experimental design, and use standardized assessment tools for violent behavior to provide a basis for optimal clinical decision-making. In the meantime, a multi-center, multi-area large sample study was carried out, and constantly adjusted to make it more accurate and more clinical to be widely used in the outpatient departments of psychiatric hospitals.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe first authors want to thank all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTing Wang: Investigation, Writing and editing, Funding Support; Ping Zhao: Methodology, Investigation. Jiaojiao Sun: Investigation and data analysis. Yaqin Zhao: Investigation, data analysis and Funding Support, Gao Lini and Nan Liu: Conceptualization and data analysis. Jia Li: Supervision, Conceptualization, Writing - review \u0026amp; editing. Lin Wang: Writing - review \u0026amp; editing, Funding Support. The author(s) read and approved the fnal manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study were supported by the 2022 annual hospital project funding fund of Yangzhou Wutai Mountain Hospital in Jiangsu Province (Lin Wang WTS2022004), (Yaqin Zhao WTS2022008), (Ting Wang WTS2022021) and the 2023 Annual Medical Scientific Research Project of Yangzhou Municipal Health Commission (Yaqin Zhao 2023-4-4).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 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.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants signed the informed consent form before entering the study, and all procedures performed in this study involving human participants were in accordance with the Declaration of HelsinkiEthics approval and consent to. This study earned the approval of the Ethics Committee of the Yangzhou Wutai Mountain Hospital (WTSLL2023025).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants provided informed consent, including consent to publish scientifc manuscripts based on collected data, in order to participate in the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eXia Zhichun, Ye Junrong, Li Sijue, et al. 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(2021). \u003cem\u003eDiversity, equity, and inclusion in academic nursing: AACN position statement\u003c/em\u003e. \u003cem\u003ehttps://www.aacnnursing.org/Portals/42/News/Position-Statements/Diversity-Inclusion.pdf\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eSpaducci G , Stubbs B , Mcneill A ,et al.Violence in mental health settings: a systematic review[J].Int J Ment Health Nurs, 2018(1).\u003c/li\u003e\n\u003cli\u003eJalil R , Dickens G L .Systematic review of studies of mental health nurses' experience of anger and of its relationships with their attitudes and practice.[J].Journal of psychiatric and mental health nursing, 2018(3).\u003c/li\u003e\n\u003cli\u003eKonttila J , Khknen O , Tuomikoski A M .Nurses' experiences of workplace violence in psychiatric nursing: a qualitative review protocol[J].JBI Evidence Synthesis, 2020, 18.\u003c/li\u003e\n\u003cli\u003eHe J., Yue Z. (2021). \u003cem\u003eExperience of workplace violence among psychiatric nurses\u003c/em\u003e(Student thesis). \u003cem\u003ehttps://www.diva-portal.org/smash/get/diva2:1580666/FULLTEXT01.pdf\u003c/em\u003e\u003c/li\u003e\n\u003cli\u003eThe Spiritual Science Society of the Chinese Medical Association. Mental illness classification scheme and diagnostic criteria in China [M]. Southeast University Press, 1995.\u003c/li\u003e\n\u003cli\u003eFusar-Poli P , Davies C , Rutigliano G , et al. Transdiagnostic Individualized Clinically Based Risk Calculator for the Detection of Individuals at Risk and the Prediction of Psychosis: Model Refinement Including Nonlinear Effects of Age[J]. Frontiers in Psychiatry, 2019, 10.\u003c/li\u003e\n\u003cli\u003eZhang Mingyuan. A Brief Psychiatric Scale (BPRS) [J]. Shanghai psychiatry, 1984 (2).\u003c/li\u003e\n\u003cli\u003eZhang Mingyuan. The Psychiatric Rating Scale Manual [M]. Hunan Science and Technology Press, 1998.\u003c/li\u003e\n\u003cli\u003eLarge MM, Nielssen O. Violence in first-episode psychosis:a systematic review and meta-analysis[J]. Schizophr Res, 2011,125(2-3):209\u0026ndash;\u003c/li\u003e\n\u003cli\u003eWang Min, Yu Rong, Tang Wenxin. Study on risk factors for violent behavior in hospitalized schizophrenia [J]. Journal of Clinical Psychiatry, 2022,32 (6): 3.\u003c/li\u003e\n\u003cli\u003eChen Fangyu, Dai Yuanyuan. Analysis of risk factors for aggressive behavior in patients with bipolar disorder [J]. 2023(5).\u003c/li\u003e\n\u003cli\u003eZhang Hongyin. Research status of the aggressive behavior of psychiatric patients in the psychiatric department [D]. Chongqing Medical University, 2014 (02).\u003c/li\u003e\n\u003cli\u003eLiu Na, Zhang Yalin, Huang Guoping. Progress in the behavioral genetics of violent aggression [J]. 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Compilation of papers of the 13th Annual Meeting of Psychiatry Branch of Zhejiang Medical Association. 2020:2.\u003c/li\u003e\n\u003cli\u003eHuang, HuiDong, MinZhang, LingZhong, Bao-LiangNg, Chee H.Ungvari, Gabor S.Yuan, ZhenMeng, XiangfeiXiang, Yu-Tao.Psychopathology and extrapyramidal side effects in smoking and non-smoking patients with schizophrenia: Systematic review and meta-analysis of comparative studies[J]. Progress in Neuro-Psychopharmacology \u0026amp; Biological Psychiatry: An International Research, Review and News Journal, 2019, 92.\u003c/li\u003e\n\u003cli\u003eRanjit, AnuKorhonen, TellervoBuchwald, JadwigaHeikkila, KaukoTuulio-Henriksson, AnnamariRose, Richard J.Kaprio, JaakkoLatvala, Antti.Testing the reciprocal association between smoking and depressive symptoms from adolescence to adulthood: A longitudinal twin study[J]. Drug and alcohol dependence, 2019, 200.\u003c/li\u003e\n\u003cli\u003eLi Q , Zhong S , Zhou J ,et al.Delusion, excitement, violence, and suicide history are risk factors for aggressive behavior in general inpatients with serious mental illnesses: A multicenter study in China - ScienceDirect[J]. Psychiatry Research, 2019, 272:130-134.\u003c/li\u003e\n\u003cli\u003eBu Yongxia. Analysis of aggressive behavior factors and nursing [J]. Journal of Mount Taishan Medical College, 2002,23(3):298-299.\u003c/li\u003e\n\u003cli\u003eWaldheter E J , Jones N T , Johnson E R ,et al.Utility of social cognition and insight in the prediction of inpatient violence among individuals with a severe mental illness[J]. Journal of Nervous \u0026amp; Mental Disease, 2005, 193(9):609-618.\u003c/li\u003e\n\u003cli\u003eRaja M , Azzoni A .Hostility and violence of acute psychiatric inpatients[J]. Clinical Practice and Epidemiology in Mental Health, 2005, 1(1):11.\u003c/li\u003e\n\u003cli\u003eQiu Yafeng, Ma Lixia. Prediction and prevention of risk factors for violent behavior in hospitalized patients with schizophrenia [J]. The Chinese Behavioral Medicine Sciences,2003, 12(006):705-705.\u003c/li\u003e\n\u003cli\u003eBo S , Abu-Akel A , Kongerslev M ,et al.Risk factors for violence among patients with schizophrenia[J]. Clinical Psychology Review, 2011, 31(5):711-726.\u003c/li\u003e\n\u003cli\u003eBSWAA , BPTYA. Pathways to aggression and violence in psychosis without longstanding antisocial behavior: A review and proposed psychosocial model for integrative clinical interventions[J]. Psychiatry Research, 2020, 293.\u003c/li\u003e\n\u003cli\u003eDai H J, Chia-Yu Su E, Uddin M,et al. Exploring associations of clinical and social parameters with violent behaviors among psychiatric patients[J]. Journal of Biomedical Informatics, 2017:S1532046417301880.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Mental Illness, Acts of Violence, Influencing Factors, Prediction Model, Nomograph","lastPublishedDoi":"10.21203/rs.3.rs-6166504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6166504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo investigate the factors influencing the violent behavior of outpatients with mental illness and establish a nomogram prediction model with Rstudio statistical software.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003ePatients who visited the Outpatient Department of Wutaishan Hospital in Yangzhou, Jiangsu Province from February to July 2024 were selected as investigation objects. By collecting data, the independent influencing factors of patients\u0026rsquo; violent behavior were screened. A multi-factor Logistic regression model was established with R software and a column graph was plotted. The model was evaluated by receiver operating characteristic (ROC), calibration and clinical decision curves (DCA), and validated by the validation group.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eAmong 430 outpatient patients with mental illness, 114 had violent behavior, with an incidence rate of 26.5%. Statistical significance was shown in many aspects of outpatients with mental illness (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05): diagnosis, education, occupation, marriage, residence, family history, payment method, relationship with patients, psychological capital, adverse childhood experience, time of visit, smoking, drinking, age, number of visits, and psychotic symptoms (the Brief Psychiatric Rating Scale, BPRS). Educational background (odds ratio (OR)\u0026thinsp;=\u0026thinsp;0.35), marriage (OR\u0026thinsp;=\u0026thinsp;0.20), BPRS (OR\u0026thinsp;=\u0026thinsp;1.10), visit time (OR\u0026thinsp;=\u0026thinsp;4.83) and smoking (OR\u0026thinsp;=\u0026thinsp;3.37) were independent risk factors for the violence of outpatients with mental illness. Among them, educational background and marital status were protective factors. Modeling and verification groups had an area under ROC curve (AUC) of 0.93 and 0.96, respectively. The calibration curve showed a good calibration degree of the model. The clinical decision curve demonstrated the good clinical effectiveness of the model.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe established risk prediction model for the violent behavior of outpatients with mental illness has good predictive efficacy, which is beneficial for clinical mental health personnel to early detect high-risk groups of violent behavior.\u003c/p\u003e","manuscriptTitle":"Establishment of a predictive model for violent behavior among outpatient patients in psychiatric hospitals","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-01 11:49:28","doi":"10.21203/rs.3.rs-6166504/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"28222741-a271-4c3f-9201-2733055f84b4","owner":[],"postedDate":"April 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-02T14:08:25+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-01 11:49:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6166504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6166504","identity":"rs-6166504","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}