METHODS: A single-arm interventional pre-and post-pilot study was conducted on a sample of healthcare lecturers and workers who are involved in supervising healthcare students. A purposive sampling technique was used to recruit 50 healthcare educators in Malaysia. The program was conducted by trained facilitators and 31 participants completed a locally validated self-rated questionnaire to measure their self-efficacy and declarative knowledge in preventing suicide; immediately before and after the intervention.
RESULTS: Significant improvement was seen in the overall outcome following the intervention, mostly in the self-efficacy domain. No significant improvement was seen in the domain of declarative knowledge possibly due to ceiling effects; an already high baseline knowledge about suicide among healthcare workers. This is an exception in a single item that assesses a common misperception in assessing suicide risk where significant improvement was seen following the program.
CONCLUSION: The online Advanced C.A.R.E. Suicide Prevention Gatekeeper Training Program is promising in the short-term overall improvement in suicide prevention, primarily in self-efficacy.
METHODS: We identified suicide data from official public-sector sources for countries/areas-within-countries, searching websites and academic literature and contacting data custodians and authors as necessary. We sent our first data request on 22nd June 2021 and stopped collecting data on 31st October 2021. We used interrupted time series (ITS) analyses to model the association between the pandemic's emergence and total suicides and suicides by sex-, age- and sex-by-age in each country/area-within-country. We compared the observed and expected numbers of suicides in the pandemic's first nine and first 10-15 months and used meta-regression to explore sources of variation.
FINDINGS: We sourced data from 33 countries (24 high-income, six upper-middle-income, three lower-middle-income; 25 with whole-country data, 12 with data for area(s)-within-the-country, four with both). There was no evidence of greater-than-expected numbers of suicides in the majority of countries/areas-within-countries in any analysis; more commonly, there was evidence of lower-than-expected numbers. Certain sex, age and sex-by-age groups stood out as potentially concerning, but these were not consistent across countries/areas-within-countries. In the meta-regression, different patterns were not explained by countries' COVID-19 mortality rate, stringency of public health response, economic support level, or presence of a national suicide prevention strategy. Nor were they explained by countries' income level, although the meta-regression only included data from high-income and upper-middle-income countries, and there were suggestions from the ITS analyses that lower-middle-income countries fared less well.
INTERPRETATION: Although there are some countries/areas-within-countries where overall suicide numbers and numbers for certain sex- and age-based groups are greater-than-expected, these countries/areas-within-countries are in the minority. Any upward movement in suicide numbers in any place or group is concerning, and we need to remain alert to and respond to changes as the pandemic and its mental health and economic consequences continue.
FUNDING: None.
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.
METHODS: Twenty patients were recruited from a public hospital and attended DBT skills groups in an outpatient clinic. Participants completed measures assessing psychological symptoms, self-harm behaviors, suicidal ideation, emotion regulation difficulties, self-compassion, and well-being pre- and post-intervention.
RESULTS: There were significant reductions in depressive symptoms, stress, and emotion regulation difficulties, as well as increases in self-compassion and well-being from pre- to post-intervention. A trend was found for decreases in frequency and types of non-suicidal self-harm behaviors, suicidal ideation, and anxiety symptoms. Qualitative content analyses of participants' feedback indicated that the vast majority of participants perceived a positive impact from the skills group, with mindfulness and distress tolerance being rated frequently as skills that were beneficial.
CONCLUSION: These preliminary findings suggest that DBT skills training is feasible and acceptable in a Muslim-majority, low resource clinical setting, and holds promise in improving clinical outcomes among BPD patients in Malaysia.
AIMS: This study aimed to determine the prevalence of mild cognitive impairment (MCI) using the Montreal Cognitive Assessment (MoCA) and its associated factors among patients diagnosed with SLE in Malaysia.
METHODS: A total of 200 SLE patients were recruited prospectively from the outpatient clinics of two tertiary hospitals in Malaysia. Standardized clinical interview was utilized to obtain information on socio-demographic characteristics. All patients were then assessed using the MoCA questionnaire for presence of cognitive impairment; the Patient Health Questionnaire 9 (PHQ-9) for presence of depressive symptoms; and the Wong-Baker Faces Pain Scale (WBFPS) for severity of pain. The evaluation of disease activity and severity were performed by the treating rheumatologists and nephrologists using the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) and Systemic Lupus International Collaborating Clinics Damage Index (SLICC DI).
RESULTS: The prevalence of MCI was 35%. The significant associated factors from the bivariate analysis were male gender (p = 0.04), educational level (p = 0.00), WBFPS score (p = 0.035) and anticardiolipin IgM (p = 0.01). Further analysis using logistic regression model found that male gender (OR = 7.43, 95% confidence interval 1.06-52.06, p = 0.04), lower educational level (OR = 4.4, 95% confidence interval 1.47-13.21, p = 0.01) and presence of anticardiolipin IgM (OR = 6.81, 95% confidence interval 1.45-32.01, p = 0.031) were associated with impaired MoCA scores. Also, increasing pain scores increased the risk of patients being affected by cognitive impairment.
CONCLUSION: Over one-third of patients with SLE in our cohort were found to have MCI. Risk factors included male gender, lower educational level, higher pain score and presence of anticardiolipin IgM. Physicians are encouraged to perform routine screening to detect cognitive dysfunction in patients with SLE in their clinical practice as part of a more comprehensive management.