RESUMEN
Increasingly, online counseling is considered to be a cost-effective and highly accessible method of providing basic counseling and mental health services. To examine the potential of online delivery as a way of increasing overall usage of services, this study looked at students' attitudes toward and likelihood of using both online and/or face-to-face counseling. A survey was conducted with 409 students from six universities in Malaysia participating. Approximately 35% of participants reported that they would be likely to utilize online counseling services but would be unlikely to participate in face-to-face counseling. Based on these results, it is suggested that offering online counseling, in addition to face-to-face services, could be an effective way for many university counseling centers to increase the utilization of their services and thus better serve their communities.
RESUMEN
The 2015 National Health and Morbidity Survey estimated that over 29% of the adult population of Malaysia suffers from mental distress, a nearly 3-fold increase from the 10.7% estimated by the NHMS in 1996 pointing to the potential beginnings of a public health crisis. This study aimed to better understand this trend by assessing depressive symptoms and their correlates in a cross-section of Malaysians. Specifically, it assesses stress, perceived locus of control, and various socio-demographic variables as possible predictors of depressive symptoms in the Malaysian context. A total of 728 adults from three Malaysian states (Selangor, Penang, Terengganu) completed Beck's depression inventory as well as several other measures: 10% of respondents reported experiencing severe levels of depressive symptoms, 11% reported moderate and 15% reported mild depressive symptoms indicating that Malaysians are experiencing high levels of emotional distress. When controlling for the influence of other variables, depressive symptoms were predictably related to higher levels of stress and lower levels of internal locus of control. Ethnic Chinese Malaysians, housewives and those engaged in professional-type occupations reported less depressive symptoms. Business owners reported more depressive symptoms. Further research should look more into Malaysians' subjective experience of stress and depression as well as explore environmental factors that may be contributing to mental health issues. It is argued that future policies can be designed to better balance individual mental health needs with economic growth.
RESUMEN
BACKGROUND: The manual diagnosis of neurodegenerative disorders such as Alzheimer's disease (AD) and related Dementias has been a challenge. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. The use of several Machine Learning algorithms to build automated diagnostic models using low-level linguistic features resulting from verbal utterances could aid diagnosis of patients with probable AD from a large population. For this purpose, we developed different Machine Learning models on the DementiaBank language transcript clinical dataset, consisting of 99 patients with probable AD and 99 healthy controls. RESULTS: Our models learned several syntactic, lexical, and n-gram linguistic biomarkers to distinguish the probable AD group from the healthy group. In contrast to the healthy group, we found that the probable AD patients had significantly less usage of syntactic components and significantly higher usage of lexical components in their language. Also, we observed a significant difference in the use of n-grams as the healthy group were able to identify and make sense of more objects in their n-grams than the probable AD group. As such, our best diagnostic model significantly distinguished the probable AD group from the healthy elderly group with a better Area Under the Receiving Operating Characteristics Curve (AUC) using the Support Vector Machines (SVM). CONCLUSIONS: Experimental and statistical evaluations suggest that using ML algorithms for learning linguistic biomarkers from the verbal utterances of elderly individuals could help the clinical diagnosis of probable AD. We emphasise that the best ML model for predicting the disease group combines significant syntactic, lexical and top n-gram features. However, there is a need to train the diagnostic models on larger datasets, which could lead to a better AUC and clinical diagnosis of probable AD.