MATERIAL AND METHODS: During the COVID-19 pandemic, an online questionnaire gathered data from 55,589 participants from 40 countries (64.85% females aged 35.80 ± 13.61; 34.05% males aged 34.90±13.29 and 1.10% other aged 31.64±13.15). Distress and probable depression were identified with the use of a previously developed cut-off and algorithm respectively.
STATISTICAL ANALYSIS: Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses and Factorial Analysis of Variance (ANOVA) tested relations among variables.
RESULTS: Probable depression was detected in 17.80% and distress in 16.71%. A significant percentage reported a deterioration in mental state, family dynamics and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (31.82% vs. 13.07%). At least half of participants were accepting (at least to a moderate degree) a non-bizarre conspiracy. The highest Relative Risk (RR) to develop depression was associated with history of Bipolar disorder and self-harm/attempts (RR = 5.88). Suicidality was not increased in persons without a history of any mental disorder. Based on these results a model was developed.
CONCLUSIONS: The final model revealed multiple vulnerabilities and an interplay leading from simple anxiety to probable depression and suicidality through distress. This could be of practical utility since many of these factors are modifiable. Future research and interventions should specifically focus on them.
MATERIALS AND METHODS: The data came from the larger COMET-G study. The study sample includes 12,792 health professionals from 40 countries (62.40% women aged 39.76 ± 11.70; 36.81% men aged 35.91 ± 11.00 and 0.78% non-binary gender aged 35.15 ± 13.03). Distress and clinical depression were identified with the use of a previously developed cut-off and algorithm, respectively.
STATISTICAL ANALYSIS: Descriptive statistics were calculated. Chi-square tests, multiple forward stepwise linear regression analyses, and Factorial Analysis of Variance (ANOVA) tested relations among variables.
RESULTS: Clinical depression was detected in 13.16% with male doctors and 'non-binary genders' having the lowest rates (7.89 and 5.88% respectively) and 'non-binary gender' nurses and administrative staff had the highest (37.50%); distress was present in 15.19%. A significant percentage reported a deterioration in mental state, family dynamics, and everyday lifestyle. Persons with a history of mental disorders had higher rates of current depression (24.64% vs. 9.62%; p
METHODS: A comprehensive literature search for research articles published between 1950 and 2023 was carried out using major databases, such as Google Scholar, Web of Science, PubMed, Scopus, PsycINFO, EMBASE, the Cochrane Library, and Medline.
RESULTS: A total of 40 research articles were selected for review. A total of 12 research articles revealed that the prevalence of suicidal behavior among caregivers ranged from 4.7% to 26%. However, the risk of suicidal behavior among people with dementia was inconsistent, as only 17 out of 28 selected studies reported the risk of suicidal behavior among people with dementia. The risk factors associated with suicidal behavior among caregivers of people with dementia could be both self-related and care receiver-related factors, whereas risk factors in people with dementia were self-related factors. Notably, greater cognitive decline, which impairs individuals' ability to carry out complex acts and planning, may lower their suicidal risk. Finally, assessment of the risk of bias indicated that 95% of the selected studies had unclear risk.
CONCLUSION: Self-related and care receiver-related factors should be assessed among caregivers of people with dementia to evaluate the risk of suicidal behavior. In addition, we recommend evaluating suicidal risk in people with dementia in the early phase of dementia when cognitive decline is less severe. However, as the majority of the selected studies had unclear risk of bias, future studies with improved methodologies are warranted to confirm our study findings.
PURPOSE: The objective of this study is to examine the social risk factors that drive older people to have suicidal feelings or tendencies and the extent to which these factors arise from the changes that occur in their social environment as a result of the process of modernization and industrialization.
METHODS: This study employed the phenomenological approach through qualitative data collection technique. A total of 20 informants comprising 10 males and 10 females of Malay, Chinese and Indian ethnicity were selected for the study using purposive sampling technique. In-depth interviews were conducted with the informants. Data were transcribed and subsequently analyzed thematically using the NVivo 11 software.
RESULTS: The findings revealed five conditions that led older people toward suicidal intentions. These include social and cultural changes, lack of social support, conflict in religious belief, influence of economic uncertainty and socio-economic status, and depression as a result of the changes in their social environment.
CONCLUSION: The implication of this research is that these factors affect older people directly as they struggle to adapt and respond to the major changes that occur in the social structure of the society they live in, stemming from the process of modernization and industrialization. Efforts to enact better policies and services for older people need to be addressed especially in developing countries based on assessment of their needs, weaknesses, strengths, and capabilities by incorporating elements of the worldview of the older people based on their experiences of daily lives.
METHODS: A search of four databases was conducted: Web of Science, PubMed, Dimensions, and Scopus for research papers dated between January 2016 and September 2021. The search keywords are 'data mining', 'machine learning' in combination with 'suicidal behaviour', 'suicide', 'suicide attempt', 'suicidal ideation', 'suicide plan' and 'self-harm'. The studies that used machine learning techniques were synthesized according to the countries of the articles, sample description, sample size, classification tasks, number of features used to develop the models, types of machine learning techniques, and evaluation of performance metrics.
RESULTS: Thirty-five empirical articles met the criteria to be included in the current review. We provide a general overview of machine learning techniques, examine the feature categories, describe methodological challenges, and suggest areas for improvement and research directions. Ensemble prediction models have been shown to be more accurate and useful than single prediction models.
CONCLUSIONS: Machine learning has great potential for improving estimates of future suicidal behaviour and monitoring changes in risk over time. Further research can address important challenges and potential opportunities that may contribute to significant advances in suicide prediction.
OBJECTIVE: To examine the risk and protective factors contributing to suicidality among undergraduate college students in seven provinces in China.
METHODS: We conducted a cross-sectional study involving 13,387 college students from seven universities in Ningxia, Shandong, Shanghai, Jilin, Qinghai, Shaanxi, and Xinjiang. Data were collected using self-report questionnaires.
RESULTS: Higher scores in the psychological strain, depression, anxiety, stress, and psychache (psychological risk factors for suicidality) and lower scores in self-esteem and purpose in life (psychological protective factors against suicidality) were associated with increased suicidality among undergraduate students in China. Demographic factors which were associated with higher risks of suicidality were female gender, younger age, bad academic results, were an only child, non-participation in school associations, and had an urban household registration. Perceived good health was protective against suicidality.
CONCLUSIONS: Knowing the common risk and protective factors for suicidality among Chinese undergraduate students is useful in developing interventions targeted at this population and to guide public health policies on suicide in China.
OBJECTIVES: This study examined the relationship between suicide attempts and bullying among school adolescents in Malaysia.
METHODS: Data from the Malaysia NHMS 2017, a nationwide study that adopted a two-stage cluster sampling design, were analysed. The survey used a self-administered questionnaire in bilingual language adapted from GSHS developed by WHO. Participants were secondary school students aged 13 -17 in all states. Descriptive and multiple logistic regression analyses were performed using IBM SPSS version 28.
RESULTS: A total of 27,497 school adolescents participated in the study. Results showed that 6.9% of school adolescents had attempted suicide. There was 16.2% of adolescents being bullied. Multiple logistic regression revealed that students who were bullied were more likely to have suicide attempts (aOR 4.827, 95% CI: 4.143, 5.624) P