OBJECTIVES: This study was conducted to investigate knowledge, attitude, and experience regarding suicidal behavior among mental health workers in Gangwon province of Korea to use as a basis for development of suicide prevention and education programs. METHODS: Questionnaires consisted of 31 questions including general knowledge regarding suicide statistics and suicide related legislation, attitude toward suicide, awareness of suicide, and individual experiences of suicidal behaviors. Chi-square and binary logistic regression analyses were used to determine differences and association among attitude toward suicide, individual experience of suicide, and sociodemographic characteristics. RESULTS: Taboo attitude toward suicide showed association with age and male. Males thought that social responsibility for suicide is necessary. Religiously non-affiliated group and people who had previous suicidal ideation and attempts showed a more permissive attitude toward suicide. CONCLUSION: The findings of this study suggest that demographic characteristics and previous individual experience of suicidal behaviors might influence attitude toward suicide. These personal characteristics should be considered in development of suicide prevention programs for mental health workers.
Education ; Gangwon-do ; Humans ; Korea* ; Logistic Models ; Male ; Mental Health* ; Social Responsibility ; Suicidal Ideation ; Suicide ; Taboo

OBJECTIVES: This study was conducted to investigate knowledge, attitude, and experience regarding suicidal behavior among mental health workers in Gangwon province of Korea to use as a basis for development of suicide prevention and education programs. METHODS: Questionnaires consisted of 31 questions including general knowledge regarding suicide statistics and suicide related legislation, attitude toward suicide, awareness of suicide, and individual experiences of suicidal behaviors. Chi-square and binary logistic regression analyses were used to determine differences and association among attitude toward suicide, individual experience of suicide, and sociodemographic characteristics. RESULTS: Taboo attitude toward suicide showed association with age and male. Males thought that social responsibility for suicide is necessary. Religiously non-affiliated group and people who had previous suicidal ideation and attempts showed a more permissive attitude toward suicide. CONCLUSION: The findings of this study suggest that demographic characteristics and previous individual experience of suicidal behaviors might influence attitude toward suicide. These personal characteristics should be considered in development of suicide prevention programs for mental health workers.
Education ; Gangwon-do ; Humans ; Korea* ; Logistic Models ; Male ; Mental Health* ; Social Responsibility ; Suicidal Ideation ; Suicide ; Taboo

BACKGROUND AND OBJECTIVES: Whether using both traditional risk factors and genetic variants for stroke as opposed to using either of the 2 alone improves the prediction of stroke risk remains unclear. The purpose of this study was to compare the predictability of stroke risk between models using traditional risk score (TRS) and genetic risk score (GRS). METHODS: We used a case-cohort study from the Korean Cancer Prevention Study-II (KCPS-II) Biobank (n=156,701). We genotyped 72 single nucleotide polymorphisms (SNPs) identified in genome-wide association study (GWAS) on the KCPS-II sub-cohort members and stroke cases. We calculated GRS by summing the number of risk alleles. Prediction models with or without GRS were evaluated in terms of the area under the receiver operating characteristic curve (AUROC). RESULTS: Sixteen out of 72 SNPs identified in GWAS showed significant associations with stroke, with an odds ratio greater than 2.0. For participants aged < 40 years, AUROCs for incident stroke were 0.58, 0.65, and 0.67 in models using modifiable TRS only, GRS only, and TRS plus GRS, respectively, showing that GRS only model had better prediction than TRS only. For participants aged ≥40 years, however, TRS only model had better prediction than GRS only model. Favorable levels of traditional risk were associated with significantly lower stroke risks within each genetic risk category. CONCLUSIONS: TRS and GRS were both independently associated with stroke risk. Using genetic variants in addition to traditional risk factors may be the most accurate way of predicting stroke risk, particularly in relatively younger individuals.
Alleles ; Epidemiologic Methods ; Genetics ; Genome-Wide Association Study ; Korea ; Odds Ratio ; Polymorphism, Single Nucleotide ; Risk Factors ; ROC Curve ; Stroke

BACKGROUND AND OBJECTIVES: Whether using both traditional risk factors and genetic variants for stroke as opposed to using either of the 2 alone improves the prediction of stroke risk remains unclear. The purpose of this study was to compare the predictability of stroke risk between models using traditional risk score (TRS) and genetic risk score (GRS). METHODS: We used a case-cohort study from the Korean Cancer Prevention Study-II (KCPS-II) Biobank (n=156,701). We genotyped 72 single nucleotide polymorphisms (SNPs) identified in genome-wide association study (GWAS) on the KCPS-II sub-cohort members and stroke cases. We calculated GRS by summing the number of risk alleles. Prediction models with or without GRS were evaluated in terms of the area under the receiver operating characteristic curve (AUROC). RESULTS: Sixteen out of 72 SNPs identified in GWAS showed significant associations with stroke, with an odds ratio greater than 2.0. For participants aged < 40 years, AUROCs for incident stroke were 0.58, 0.65, and 0.67 in models using modifiable TRS only, GRS only, and TRS plus GRS, respectively, showing that GRS only model had better prediction than TRS only. For participants aged ≥40 years, however, TRS only model had better prediction than GRS only model. Favorable levels of traditional risk were associated with significantly lower stroke risks within each genetic risk category. CONCLUSIONS TRS and GRS were both independently associated with stroke risk. Using genetic variants in addition to traditional risk factors may be the most accurate way of predicting stroke risk, particularly in relatively younger individuals.