Reliability and Validity of the Beck Scale for Suicide Ideation (BSS) in Korean Adult Participants

et al. 2021

Front. Psychiatry

While there has been a slew of review studies on suicide measurement tools until now, there were not any reviews focusing on suicide assessment tools available in Korea. This review aimed to examine the psychometric properties of tools developed in Korea or the translated versions from the original tools in their foreign language and to identify potential improvements and supplements for these tools. A literature search was done using the Korean academic information search service, Research Information Service System, to identify the suicide measures to be included in this review. Abstracts were screened to identify which measures were used to assess suicide-related factors. Based on the established inclusion and exclusion criteria, 18 tools remained and we assessed their psychometric properties. The current review indicated several major findings. First, many of the tools did not report predictive validity and even those with predictive validity were based on past suicide attempts. Second, some of the tools overlooked the interactive component for the cause of suicide. In addition, information to supplement the self-reported and clinician-administered reports by collecting reports from the subjects' families and acquaintances is needed. It is also important to develop a screening tool that examines other aspects of an individual's personal life, including unemployment, bereavement, divorce, and childhood trauma. Moreover, tools that have been studied in more diverse groups of the population are needed to increase external validity. Finally, the linguistic translation of the tools into Korean needs to consider other cultural, social, and psychological factors of the sample of interest.

Section: Resultsmentioning

confidence: 96%

A Review of Suicide Risk Assessment Tools and Their Measured Psychometric Properties in Korea

Baek

et al. 2021

Front. Psychiatry

“…Patients were classified as being at high risk of suicide based on their SSI scores. The SSI was used for the following reasons: (1) earlier studies have established its high internal consistency, with a high Cronbach α ranging from 0.8 to 0.9, test-retest stability, and concurrent validity [ 39 , 48 , 49 ]; (2) SSI is modeled on the interviewer-rated Scale for Suicide Ideation, which is one of the few suicide scales with reported predictive validity for completed suicide [ 50 ]; and (3) SSI has been proven to be measurement-invariant over time, which is of particular importance for our longitudinal analysis [ 37 ]. In fact, a systematic review of the most commonly used suicide assessment instruments concluded that the SSI is one of the most reliable instruments for its psychometric properties, clinical utility, and cultural appropriateness [ 51 ].…”

Section: Methodsmentioning

confidence: 99%

Acoustic Analysis of Speech for Screening for Suicide Risk: Machine Learning Classifiers for Between- and Within-Person Evaluation of Suicidality

Min¹,

Shin²,

Rhee³

et al. 2023

J Med Internet Res

Background Assessing a patient’s suicide risk is challenging for health professionals because it depends on voluntary disclosure by the patient and often has limited resources. The application of novel machine learning approaches to determine suicide risk has clinical utility. Objective This study aimed to investigate cross-sectional and longitudinal approaches to assess suicidality based on acoustic voice features of psychiatric patients using artificial intelligence. Methods We collected 348 voice recordings during clinical interviews of 104 patients diagnosed with mood disorders at baseline and 2, 4, 8, and 12 months after recruitment. Suicidality was assessed using the Beck Scale for Suicidal Ideation and suicidal behavior using the Columbia Suicide Severity Rating Scale. The acoustic features of the voice, including temporal, formal, and spectral features, were extracted from the recordings. A between-person classification model that examines the vocal characteristics of individuals cross sectionally to detect individuals at high risk for suicide and a within-person classification model that detects considerable worsening of suicidality based on changes in acoustic features within an individual were developed and compared. Internal validation was performed using 10-fold cross validation of audio data from baseline to 2-month and external validation was performed using data from 2 to 4 months. Results A combined set of 12 acoustic features and 3 demographic variables (age, sex, and past suicide attempts) were included in the single-layer artificial neural network for the between-person classification model. Furthermore, 13 acoustic features were included in the extreme gradient boosting machine learning algorithm for the within-person model. The between-person classifier was able to detect high suicidality with 69% accuracy (sensitivity 74%, specificity 62%, area under the receiver operating characteristic curve 0.62), whereas the within-person model was able to predict worsening suicidality over 2 months with 79% accuracy (sensitivity 68%, specificity 84%, area under receiver operating characteristic curve 0.67). The second model showed 62% accuracy in predicting increased suicidality in external sets. Conclusions Within-person analysis using changes in acoustic features within an individual is a promising approach to detect increased suicidality. Automated analysis of voice can be used to support the real-time assessment of suicide risk in primary care or telemedicine.

“…The Korean version of the Beck Scale for Suicide Ideation (K‐BSS) will be used to measure the risk of suicidal ideation (Beck et al, 1988 ; Choi et al, 2020 ). It is offered by Korea Psychology, Co., Ltd. and is a self‐report instrument asking an individual's wish to live, wish to die, frequency of ideation, perceived capability to carry out an attempt and extent of actual preparation.…”

Section: The Studymentioning

confidence: 99%

Ecological momentary assessment of mental health in adults at suicide risk: An observational study protocol

Park

Journal of Advanced Nursing

et al. 2022

Aims:To describe the research protocol for an ecological momentary assessment (EMA) designed to examine patterns of suicidal ideation and relevant psychosocial stress indicators in adults at risk for suicide.Design: This observational and longitudinal study will collect data for 28 consecutive days.Methods: A total of 150 adults at risk for suicide will be recruited from a single suicide prevention centre and an outpatient clinic in Korea. Self-report questionnaires will be administrated during weeks 0, 1, 3 and 5. Participants will receive text messages three times a day for 4 weeks prompting them to access an online survey link for daily mood survey including depression, anxiety, stress and suicidal ideation. In addition, for the first 2 weeks, they will wear an actigraphy device designed to collect actigraphic data in terms of sleep patterns and physical activity. Data analyses such as descriptive statistics, independent t-tests, one-way ANOVA, chi-squared statistics and time-series and correlation analyses will be performed using IBM SPSS 26.0 and SAS version 9.3.