Suicide Risk Assessment Research Articles

Effectiveness of Integrating Suicide Care in Primary Care : Secondary Analysis of a Stepped-Wedge, Cluster Randomized Implementation Trial.

Primary care encounters are common among patients at risk for suicide. To evaluate the effectiveness of implementing population-based suicide care (SC) in primary care for suicide attempt prevention. Secondary analysis of a stepped-wedge, cluster randomized implementation trial. (ClinicalTrials.gov: NCT02675777). 19 primary care practices within a large health care system in Washington State, randomly assigned launch dates. Adult patients (aged ≥18 years) with primary care visits from January 2015 to July 2018. Practice facilitators, electronic medical record (EMR) clinical decision support, and performance monitoring supported implementation of depression screening, suicide risk assessment, and safety planning. Clinical practice and patient measures relied on EMR and insurance claims data to compare usual care (UC) and SC periods. Primary outcomes included documented safety planning after population-based screening and suicide risk assessment and suicide attempts or deaths (with self-harm intent) within 90 days of a visit. Mixed-effects logistic models regressed binary outcome indicators on UC versus SC, adjusted for randomization stratification and calendar time, accounting for repeated outcomes from the same site. Monthly outcome rates (percentage per 10 000 patients) were estimated by applying marginal standardization. During UC, 255 789 patients made 953 402 primary care visits and 228 255 patients made 615 511 visits during the SC period. The rate of safety planning was higher in the SC group than in the UC group (38.3 vs. 32.8 per 10 000 patients; rate difference, 5.5 [95% CI, 2.3 to 8.7]). Suicide attempts within 90 days were lower in the SC group than in the UC group (4.5 vs. 6.0 per 10 000 patients; rate difference, -1.5 [CI, -2.6 to -0.4]). Suicide care was implemented in combination with care for depression and substance use. Implementation of population-based SC concurrent with a substance use program resulted in a 25% reduction in the suicide attempt rate in the 90 days after primary care visits. National Institute of Mental Health.

Teacher emotionally intelligent behaviors and its role in the link between cybervictimization and suicide risk factors in adolescents

Psychological distress is an increasing concern among adolescents worldwide. Cybervictimization is considered one of the stressors that adolescents may face, and it significantly impacts various indicators of suicide risk (i.e., symptoms of depression, anxiety, and stress, and suicidal ideation). However, teacher emotionally intelligent behaviors (TEIB) might mitigate the links between cybervictimization experiences and suicide risk factors. This study aimed to examine the relationships between cybervictimization and several suicide risk indicators, as well as the potential moderating role of TEIB in these links. A sample of 1,866 (996 girls) adolescents (Mage = 13.99, SD = 1.44) participated in this study and completed widely validated measures. Moderation analyses revealed that TEIB moderated the positive associations between cybervictimization and symptoms of depression, anxiety, and stress, even after controlling for the effects of sociodemographic factors (i.e., age and gender) and emotional intelligence. However, TEIB did not moderate the link between cybervictimization and suicidal ideation. These novel findings both underscore the significance of students' perceptions of TEIB in shaping their psychological well-being and highlight potential avenues for research aimed at mitigating the consequences of cybervictimization.

Validation of the Spanish Version of the Nurses' Global Assessment of Suicide Risk Scale (NGAR) in Nonclinical Settings.

To validate the Nurses' Global Assessment of Suicide Risk (NGASR) in Spanish for an early detection identification of the risk of suicide. A descriptive cross-sectional survey design was used for this work through face-to-face clinical interviews with each participant. Following EQUATOR TRIPOD checklist, the index was translated and administered to a sample of 30 mental health experts and 151 university students. To examine the psychometric properties of the NGASR, the questionnaires also included other standardised scales such as BDI, SBQ and SEEQ. The research was conducted between 2022 and 2023. The content validity index-scale (CVI-S) was 81% and the NGASR presented high reliability with a Kuder-Richardson coefficient of 0.83. Exploratory factor analysis (EFA) returned a six-factor structure for the NGASR items. The results showed that 21.7% of the students assessed had an intermediate to very high suicide risk. This study also revealed that people with mental health problems and depression had a higher risk of suicide. Beyond the sociolinguistic Spanish validation of the scale, it should be noted that it is carried out on a young population in a nonclinical environment, something that many authors have been requesting in their previous validations. The NGASR is a useful prevention tool in university educational settings.

A study into the mental health of PhD students in Australia: investigating the determinants of depression, anxiety, and suicidality.

PhD students are particularly vulnerable to experiencing poor mental health. Mental health concerns that arise during their studies can not only affect their study progress but also have long-lasting impacts on their mental health after their studies. Extensive research has been conducted on the mental health of university students, but few studies have focused on PhD students and even fewer on PhD students in Australia. The present study investigated demographic, occupational, psychological, social, and relational determinants of mental health symptoms (depression, anxiety, and suicidality) in PhD students in Australia. PhD students enrolled in Australian universities were invited to complete an online survey. Results from regression analyses identified key determinants of mental health symptoms in our sample of PhD students (N = 302). In particular, higher levels of imposter thoughts, perfectionism discrepancy, and loneliness were strong predictors of depression, anxiety, and suicidality. These findings contribute to our understanding of the mental health of PhD students in Australia. Importantly, these findings inform areas of focus where potential strategies can be implemented to better protect the mental health of this population. For example, strategies that mitigate loneliness or foster effective, collaborative student-supervisor relationships.

Evaluation of Suicidality in Psychiatric Outpatients: Experience of Ar-razi Hospital in Salé

Suicidality, encompassing suicidal ideation, plans, and suicide attempts, is a complex phenomenon particularly prevalent among psychiatric patients. This study focuses on identifying the risk factors associated with suicidality in outpatients at a psychiatric hospital in Morocco. A total of 278 patients were evaluated using a questionnaire covering sociodemographic data, psychiatric history, and suicidal behaviors. The results show that gender, religious practices, mood disorders, substance use, particularly alcohol and benzodiazepines, and a history of suicide attempts are significant predictors of suicidality. These findings highlight the importance of a multidimensional approach to suicide assessment and prevention in this population.

Neighborhood predictors of suicide and firearm suicide in Detroit, Michigan

BackgroundSuicide is a leading cause of death in the United States with rates increasing over the past two decades. The rate of suicide is higher in rural areas, but a greater number of people in urban areas die by suicide; understanding risk factors for suicide in this context is critically important to public health. Additionally, while many studies have focused on individual-level risk factors, few studies have identified social or structural features associated with suicide or firearm suicide, especially among young people.MethodsStudy outcomes included total firearm suicide, total youth (age 10–29) firearm suicide, total suicide, and total youth suicide in Detroit, Michigan from 2012 through 2019. The predictors in this study included 58 census-tract level variables characterizing the physical features, residential stability, socioeconomic status, and demographics of neighborhoods in Detroit over the study period. We used random forest, extreme gradient boosting (XGBoost), and generalized linear mixed models to predict the four outcomes.ResultsWe found that the tract-level variables used in all three modeling approaches performed poorly at predicting the suicide outcomes, with area under the curve values at times exceeding 0.60 but with extremely low sensitivity (ranging from 0.05 to 0.45). However, the percentage of parcels sold in arms-length transfers in the previous 5 years, the count of vacant lots per square mile, and the percentage of children aged three and older who were enrolled in preschool each demonstrated associations with at least two of the outcomes studied.ConclusionsOur findings suggest place-based factors at the tract level do not provide meaningful insight into the risk of suicide or firearm suicide among youth or the general population in Detroit, Michigan. Future practice and study should consider focusing on both larger and smaller areas, including city and individual-level factors. For example, studies might benefit from the use of both neighborhood and individual-level measures and their interactions to improve our understanding of place-based risk factors and suicide risk.

Using Natural Language Processing to develop risk-tier specific suicide prediction models for Veterans Affairs patients

Suicide is a leading cause of death. Suicide rates are particularly elevated among Department of Veterans Affairs (VA) patients. While VA has made impactful suicide prevention advances, efforts primarily target high-risk patients with documented suicide risk. This high-risk population accounts for less than 10% of VA patient suicide deaths. We previously evaluated epidemiological patterns among VA patients that had lower classified suicide risk and derived moderate- and low-risk groupings. Expanding upon VA's leading suicide prediction model, this study uses national VA data to refine high-, moderate-, and low-risk specific suicide prediction methods. We selected all VA patients who died by suicide in 2017 or 2018 (n = 4584), matching each case with five controls who remained alive during treatment year and shared suicide risk percentiles. We extracted all sample unstructured electronic health record notes, analyzed them using natural language processing, and applied machine-learning classification algorithms to develop risk-tier-specific predictive models. We calculated area under the curve (AUC) and suicide risk concentration to evaluate predictive accuracy and analyzed derived words. ResultsOur high-risk model (AUC = 0.621 (95% CI: 0.55–0.68)), moderate-risk (AUC = 0.669 (95% CI: 0.64–0.71)), and low-risk (AUC = 0.673 (95% CI: 0.63–0.72)) models offered significant predictive accuracy over VA's leading suicide prediction algorithm. Derived words varied considerably, the high-risk model including chronic condition service words, moderate-risk model including outpatient care, and low-risk model including acute condition care.Study suggests benefit of leveraging unstructured electronic health records and expands prediction resources for non-high-risk suicide decedents, an historically underserved population.

Trauma History in a Medium-Secure Adult Male Forensic Mental Health Setting

Forensic services have shifted their focus to the importance of forensic practice becoming more trauma informed. Trauma exposure is pervasive in forensic mental health populations and has been found to negatively impact the course of severe mental illness (SMI) and increase risk of violence. This study aimed to explore the trauma histories of adult male inpatients in a medium-secure psychiatric hospital, and examine childhood and adulthood trauma as predictors of diagnoses, self-harm, suicidality, hospital re-admissions, substance use, and nature of offense. The case files of 85 male patients in a medium secure unit in the UK were reviewed. Eighty-seven percent of patients had a schizophrenia diagnosis and 86% had extensive trauma histories, the most common trauma type being physical violence in childhood (44.7%). One patient had a PTSD diagnosis. Logistic regressions demonstrated that cumulative childhood and adulthood trauma significantly increased the odds of self-harm. Cumulative childhood trauma exposure was found to decrease the likelihood of having a schizophrenia diagnosis. There is a serious under-recognition of the extent and consequences of trauma exposure; trauma histories are likely greater than the findings of this study and existing literature suggests. Implications for clinical practice, trauma-informed care (TiC), and future research are proposed.

Suicidal Behavior and Personality Group Affiliation: Approaches and Research Directions

Introduction. As it follows from a number of scientific works, not only suicide rates, but also their motives and ways of committing suicide vary widely in different countries, strata of society and strata of the population. In recent decades, research on this issue has become interdisciplinary and cross-cultural. Suicidal behavior is increasingly considered in the context of the suicidal person’s belonging to a certain social group or cultural community. Objective. To analyze the impact of an individual’s membership in a large social group or cultural community on suicidal behavior. The main directions of suicidal behavior research. In the vast array of theoretical and research works the following directions of suicidal behavior study can be distinguished: existential, clinical, socio-demographic, socio-cultural and socio-psychological. Starting from E. Durkheim’s sociological etude “Suicide”, the group affiliation of an individual is considered as a leading factor of suicidal behavior. However, the sociological approach appears limited because its proponents place excessive emphasis on demographic variables rather than on the psychological significance of suicide and the values of the individual prone to suicidal behavior. A. Adler’s socio-psychological conceptualization of suicide is partly responsible for overcoming these limitations. Adler’s socio-psychological concept of suicide, which takes into account not only demographic indicators, but also how suicides perceive, evaluate and experience their own group affiliation and the values of their social group. According to A. Adler, the predictors of suicide are a sense of community (belonging) and social interests of the individual. Sense of community and group belonging as a factor of suicidal behavior. Comparison of studies conducted in three countries (Russia / USSR, Japan and the USA) allows us to identify two socio-psychological factors of suicidal behavior: 1) negative attitude to one’s own group or cultural identity; 2) hypertrophy of the sense of community and belonging, leading to a decrease in the value of one’s own personality and individual life. Discussion. As modern cross-cultural studies show, suicidal behavior can be observed both at the deficit of the sense of community and group belonging and at the high level of development of this sense. This is conditioned, on the one hand, by the attitude of a suicide victim to his/her social group and cultural identity, and, on the other hand, by the historically formed attitude of society to suicide.

Typologies of Psychiatric Diagnoses Among Inpatients with Recent Suicide Attempts

Objective Psychiatric multimorbidity is a well-documented risk factor for suicide. However, diagnostic heterogeneity and patterns of comorbidity likely exists within the population of those who attempt suicide. Person-centered statistical approaches, such as latent class analysis (LCA), extract distinguishable groups differentiated by prevalence and comorbidity of psychiatric disorders. Method The present study used LCA to identify typologies of psychiatric heterogeneity in a sample of 213 inpatients (M age = 33.04 [SD = 12.67]; 57.3% female; 62.4% White; 23.9% Hispanic/Latino) with a history of suicide attempt who were recruited for a suicide prevention clinical trial. Class differences in suicide history characteristics; demographic characteristics; and cognitive-affective and behavioral risk factors, obtained from an initial evaluation involving the administration of a semi-structured diagnostic interview, suicide risk assessment, and battery of self-report measures, were explored. Results LCA identified three classes in the best-fitting solution: Depressive-High Comorbidity (n = 68), Depressive-Low Comorbidity (n = 86), and Bipolar (n = 59). The Depressive-Low Comorbidity class reported less severe suicidal ideation (p < .001), anxiety (p < .001), stress (p < .001), unlovability beliefs (p = .006), and impulsivity (p < .001). The Depressive-Low Comorbidity class also reported fewer actual attempts than the Bipolar class (p = .001) and fewer interrupted attempts than the Depressive-High Comorbidity class (p = .004). Conclusions The Depressive-High Comorbidity and Bipolar classes consistently endorsed higher levels of suicide risk factors. These findings may help to illuminate typologies of suicide attempters with unique clinical needs, which is an essential step toward personalized medicine.

The Suicide Status Form-4 (SSF-IV) as a potentially therapeutic suicide risk assessment tool.

Empirically supported suicide risk assessment and conceptualization is a central aim of the Zero Suicide model. The Suicide Status Form (SSF) is the essential document and scaffolding of the Collaborative Assessment and Management of Suicidality-Brief Intervention (CAMS-BI) and is hypothesized as an example of a psychological assessment as therapeutic intervention (PATI). However, this hypothesis has never been directly tested. N = 57 patients deemed at risk for outpatient suicidal behavior and treated as part of an inpatient psychiatric consultation and liaison service were recruited to participate in CAMS-BI at a Level 1 trauma center in the southeastern United States. During the CAMS-BI process, patients were asked to rate their subjective units of distress (SUDS) at five time points throughout the intervention (k = 285). The omnibus random intercept multilevel model revealed a significant difference in pre- to post-session ratings of SUDS across patients. Post hoc pairwise comparisons revealed no significant differences between SSF sections (e.g., Section A, Section B, and Section C) and relative reductions in SUDS; however, there was an observable trend toward a favorable effect of Section A of the SSF. The SSF may represent an example of PATI pending replication and extension of the current results.

Late-life suicide: machine learning predictors from a large European longitudinal cohort.

People in late adulthood die by suicide at the highest rate worldwide. However, there are still no tools to help predict the risk of death from suicide in old age. Here, we leveraged the Survey of Health, Ageing, and Retirement in Europe (SHARE) prospective dataset to train and test a machine learning model to identify predictors for suicide in late life. Of more than 16,000 deaths recorded, 74 were suicides. We matched 73 individuals who died by suicide with people who died by accident, according to sex (28.8% female in the total sample), age at death (67 ± 16.4 years), suicidal ideation (measured with the EURO-D scale), and the number of chronic illnesses. A random forest algorithm was trained on demographic data, physical health, depression, and cognitive functioning to extract essential variables for predicting death from suicide and then tested on the test set. The random forest algorithm had an accuracy of 79% (95% CI 0.60-0.92, p = 0.002), a sensitivity of.80, and a specificity of.78. Among the variables contributing to the model performance, the three most important factors were how long the participant was ill before death, the frequency of contact with the next of kin and the number of offspring still alive. Prospective clinical and social information can predict death from suicide with good accuracy in late adulthood. Most of the variables that surfaced as risk factors can be attributed to the construct of social connectedness, which has been shown to play a decisive role in suicide in late life.

Ethical and Data Security Concerns in the Use of Natural Language Processing for Early Detection and Risk Assessment in Mental Health

Ethical and Data Security Concerns in the Use of Natural Language Processing for Early Detection and Risk Assessment in Mental Health

Artificial intelligence-based suicide prevention and prediction: A systematic review (2019–2023)

Suicide is a major global public health concern, and the application of artificial intelligence (AI) methods, such as natural language processing (NLP), machine learning (ML), and deep learning (DL), has shown promise in advancing suicide prediction and prevention efforts. Recent advancements in AI – particularly NLP and DL have opened up new avenues of research in suicide prediction and prevention. While several papers have reviewed specific detection techniques like NLP or DL, there has been no recent study that acts as a one-stop-shop, providing a comprehensive overview of all AI-based studies in this field. In this work, we conduct a systematic literature review to identify relevant studies published between 2019 and 2023, resulting in the inclusion of 156 studies. We provide a comprehensive overview of the current state of research conducted on AI-driven suicide prevention and prediction, focusing on different data types and AI techniques employed. We discuss the benefits and challenges of these approaches and propose future research directions to improve the practical application of AI in suicide research. AI is highly capable of improving the accuracy and efficiency of risk assessment, enabling personalized interventions, and enhancing our understanding of risk and protective factors. Multidisciplinary approaches combining diverse data sources and AI methods can help identify individuals at risk by analyzing social media content, patient histories, and data from mobile devices, enabling timely intervention. However, challenges related to data privacy, algorithmic bias, model interpretability, and real-world implementation must be addressed to realize the full potential of these technologies. Future research should focus on integrating prediction and prevention strategies, harnessing multimodal data, and expanding the scope to include diverse populations. Collaboration across disciplines and stakeholders is essential to ensure that AI-driven suicide prevention and prediction efforts are ethical, culturally sensitive, and person-centered.

Knowledge, Attitude, and Practice of Nursing Staff Toward Patients Who Attempted Suicide at Tertiary Care Hospital of Central Gujarat, India

Background: Looking at the alarming increase in the incidence of attempted suicide worldwide, having adequate expertise in managing people with attempted suicide is imperative. Therefore, healthcare professionals, specifically nurses, must be provided training exclusively on managing patients with attempted suicide and preventing suicide in the near and distant future. Nursing staff play a major role as a point of first contact for suicide survivors, and therefore, it is of utmost importance that they are adequately trained and knowledgeable in this area. To understand their knowledge, the authors decided to do this study to measure knowledge, attitude, and practice (KAP) among nurses in suicide survivors. Aim: The aim of this article was to study the KAP of nursing staff toward patients with suicide attempts. Methods: A hospital-based cross-sectional, quantitative study was conducted among nursing staff. All nurses ( n = 300) were approached in small groups between July and November 2023 via a convenient sampling. Written informed consent from participants was taken. The semi-structured questionnaire included sociodemographic details, their professional experience, their training and experience in managing suicidal patients, and KAP questions. The KAP questions were on patients with suicide attempts and were derived from the suicide knowledge and skill questionnaire (SKSQ), suicide attitude questionnaire, and practice guidelines of the American Psychiatric Nurses Association. Data was analyzed by using MS Excel version 6.2.14. Result: In this study, a total of 200 nursing staff participated. The majority were female (80%), with a mean age of 37 years and a mean professional experience of 12 years. Out of all participants, 68% had experience managing patients with suicide attempts, but only 27% of them were trained in suicide prevention. The mean score of knowledge and attitude among study participants were 65.25/100 and 3.24/5, respectively. Nursing staff had higher scores in questions of suicide risk assessment and statements related to attitudes like “they deserve respect in society.” In practice assessment, more than 80 % of nurses were keenly observant, empathetic, and nonstigmatized for hospitalized patients of suicide attempts. The nurses took measures like restricting access to harmful objects (medicines, sharp objects) and giving medicines under supervision. Conclusion: The nursing staff had adequate knowledge, a nonstigmatized attitude, and appropriate clinical practices toward patients with suicide attempts, but they lacked formal training. So, hands-on training for suicide management at regular intervals is the need of the hour.

Association Between Impulsivity, Self-Harm, Suicidal Ideation, and Suicide Attempts in Patients with Traumatic Brain Injury.

Traumatic brain injury (TBI) affects over 48 million people worldwide each year. Suicide is common in TBI, and there are several known contributing factors, including severe TBI, depression, alcohol use, and male sex. Impulsivity, or the tendency to act quickly with little thought, may be an early predictor of suicidality following TBI. The purpose of this study was to evaluate the risk of suicidality in patients with a prior history of impulsivity following a traumatic brain injury (TBI). Using de-identified electronic health records from the TriNetX United States Collaborative Network with Natural Language Processing, three cohorts were generated: the impulsivity TBI cohort (I+TBI+) included subjects with a diagnosis of impulsivity before a diagnosis of TBI; the no impulsivity TBI cohort (I-TBI+) included patients with TBI but no impulsivity; the impulsivity no TBI cohort (I+TBI-) included patients with impulsivity but TBI. Two analyses were conducted, including analysis 1 (impulsivity TBI vs. no impulsivity TBI) and analysis 2 (impulsivity TBI vs. impulsivity no TBI). Patients were 1:1 propensity score matched by age, sex, race, ethnicity, psychiatric diagnoses, and antidepressant use. Outcomes included a diagnosis of self-harm, suicidal ideation, or a suicide attempt within one year after the index event. The all-time incidence of each outcome was assessed across different age categories. The chi-square test (categorical variables) and t-test (numerical variables) were used to assess for differences between groups. A total of 1,292,776 TBI patients were identified in the study. After 1:1 propensity score-matching, there were 20,694 patients (mean [SD] age, 48.1 [21.8]; 8,424 females [40.7%]) with impulsivity and TBI (I+TBI+), 1,272,082 patients (mean [SD] age, 46.0 [23.1]; 562,705 females [44.2%]) with TBI alone (I-TBI+), and 90,669 patients (mean [SD] age, 43.7 [22.6]; 45,188 females [49.8%]) with impulsivity alone (I+TBI-). Within the first year after a TBI, patients with impulsivity were more likely to exhibit self-harm (P < 0.001), suicidal ideation (P < 0.001) or a suicide attempt (P < 0.001). Compared to TBI patients without impulsivity, those with impulsivity had a four-fold increase in the incidence of self-harm (2.81% vs. 0.63%), an eight-fold increase in suicidal ideation (52.42% vs. 6.41%), and a twenty-one-fold increase in suicide attempts (32.02% vs. 1.50%). This study suggests that impulsivity diagnosed before a TBI may increase the risk of post-traumatic suicidality, with a four-fold increased risk of self-harm, an eight-fold increased risk of suicidal ideation and a twenty-one-fold increased risk of suicide attempts. This characterizes a group of at-risk individuals who may benefit from early psychiatric support and targeted interventions following a TBI.

Facial image analysis for automated suicide risk detection with deep neural networks

Accurately assessing suicide risk is a critical concern in mental health care. Traditional methods, which often rely on self-reporting and clinical interviews, are limited by their subjective nature and may overlook non-verbal cues. This study introduces an innovative approach to suicide risk assessment using facial image analysis. The Suicidal Visual Indicators Prediction (SVIP) Framework leverages EfficientNetb0 and ResNet architectures, enhanced through Bayesian optimization techniques, to detect nuanced facial expressions indicating mental state. The models’ interpretability is improved using GRADCAM, Occlusion Sensitivity, and LIME, which highlight significant facial regions for predictions. Using datasets DB1 and DB2, which consist of full and cropped facial images from social media profiles of individuals with known suicide outcomes, the method achieved 67.93% accuracy with EfficientNetb0 on DB1 and up to 76.6% accuracy with a Bayesian-optimized Support Vector Machine model using ResNet18 features on DB2. This approach provides a less intrusive, accessible alternative to video-based methods and demonstrates the substantial potential for early detection and intervention in mental health care.

A systematic review of suicide risk management strategies in primary care settings.

Suicide is a major public health concern. Recently, suicide rates have increased among traditionally low-risk groups (e.g., white, middle-aged males). Suicide risk assessments and prevention strategies should be tailored to specific at-risk populations. This systematic review examines suicide risk detection and management in primary care, focusing on treatments to reduce suicide rates and improve prevention efforts. A systematic review was conducted following PRISMA guidelines. Literature was collected and analyzed using Boolean operators with relevant keywords in databases (e.g., PubMed, Google Scholar, PsycINFO) to identify randomized and non-randomized studies focusing on suicide risk factors and management strategies in primary care, published in the past 10 years. The risk of bias 2.0 and Newcastle Ottawa scale was used to assess risk of bias, and data from moderate-quality studies were synthesized. Thirteen moderate-quality studies were reviewed. Key findings include the need for assessing modifiable risk factors like substance use and mental health. General practitioner (GP) engagement post-suicide attempt (SA) improves outcomes and reduces repeat SAs. Effective strategies include comprehensive risk assessments, collaborative treatment, and enhanced GP support. Barriers to effective suicide prevention include insufficient information, judgmental communication, lack of positive therapeutic relationships, and inadequate holistic assessments. These findings highlight the need for tailored suicide prevention strategies in primary care. However, the evidence sample size is small with reduced statistical power that limits generalizability. The included studies were also regional examinations, which restrict their broader relevance. Significant risk factors, barriers, and effective strategies for suicide prevention were identified. For children aged 12 or younger, preexisting psychiatric, developmental, or behavioral disorders, impulsive behaviors, aggressiveness, and significant stressful life events within the family were critical. For adults, loneliness, gaps in depression treatment, and social factors are significant. Barriers to suicide prevention included insufficient information, judgmental communication, lack of positive therapeutic relationships, inadequate holistic risk assessments, lack of individualized care, insufficient tangible support and resources, inconsistent follow-up procedures, variability in risk assessment, poor communication, stigma, and negative attitudes. Effective methods include the Postvention Assisting Bereaved by Suicide training program, continued education, comprehensive clinical assessments, individualized care, and community-based interventions like the SUPRANET program. https://www.crd.york.ac.uk/PROSPERO, identifier CRD42024550904.

The Importance of Time: Nurses' Experiences of Working With Suicide Prevention and Suicide Risk Assessment in the Care of Older People

ABSTRACTAimTo describe nurses' experiences of working with suicide prevention and suicide risk assessment in the care of older people.BackgroundSuicide is a problem among older people, worldwide. Risk factors for suicide are, among others, depression, illness and pain and a feeling of isolation. Nurses have valuable experience in suicide prevention and risk assessments. This study can shed light on what is needed to promote the mental health of older people, which has been partly overlooked in previous research.Methods/DesignA descriptive qualitative study was conducted. Ten nurses working in care of older people across four different municipalities were interviewed. Conventional content analysis was used to analyse the data.ResultsDespite shortcomings in guidelines, nurses describe performing suicide risk assessments and suicide prevention in a similar manner. The importance of giving the patients time is emphasised, although time is exactly what appears to be lacking.ConclusionBy engaging with the experiences of nurses, important aspects that require further attention are highlighted. Nurses should inquire about suicidal ideation, even if older persons do not explicitly express such thoughts. Normal ageing needs to be comprehended and considered in assessments. In this work, time is important.Patient or Public ContributionThe results were coproduced by means of interviews with nurses working in care of older people.Reporting MethodThe study was conducted in accordance with the Consolidated Criteria for Reporting Qualitative Research (COREQ) guidelines.Implications for PracticeEncountering older people who may have experienced the loss of loved ones and addressing one's personal capabilities in the context of the ageing body imposes substantial challenges on nursing care, necessitating a capacity to adeptly navigate and address these complex issues.

Suicidal thoughts and attempts in a transdiagnostic eating disorder sample: Do diagnostic severity criteria predict risk?

Eating disorders (EDs) are associated with elevated suicide. Low body mass index (BMI) and frequency of purging and binge eating represent severity criteria for EDs and distinguish full-threshold EDs from other specified feeding and eating disorders (OSFED). However, no work has taken a transdiagnostic approach to studying whether severity of these or other features is associated with suicidal ideation (SI) and attempts. We examined diagnostic status, ED features, and SI and attempts in a large, transdiagnostic, community sample of 257 women with EDs and 45 controls without a current or past ED in the United States using the EDs Examination interview and the Structured Clinical Interview for the DSM-5 (Diagnostic and Statistical Manual of Mental Disorders). SI and suicide attempts (SA) were elevated in OSFED compared to controls but did not differ between OSFED and full-threshold EDs. Higher BMI predicted increased SI. Number of purging methods, but not frequency, was related to history of SA. Binge episode frequency and size were not significant predictors. OSFED presents with elevated SI and SA, and ED severity criteria that distinguish OSFED from full-threshold EDs do not predict SI or SA. Suicide risk assessments should be implemented universally across EDs in clinical practice.