Differential item functioning of the Beck Anxiety Inventory in a rural, multi-ethnic cohort

The traditional Chinese version of the Geriatric Anxiety Inventory: Psychometric properties and cutoff point for detecting anxiety

The study aimed to translate the Geriatric Anxiety Inventory into traditional Chinese (GAI-TC), examine its psychometric properties, and identify the optimal cutoff point. This research recruited 337 older adults from two community activity centers. Structured questionnaires were used, including demographic information and characteristics, the GAI-TC, and the State-Trait Anxiety Inventory (STAI). Cronbach's α of the GAI-TC was 0.93. The intraclass correlation coefficient was 0.90. The content validity index was 1.0. An exploratory factor analysis revealed that three factors in the GAI-TC, including cognition anxiety, impact of anxiety, and somatic anxiety, explained 59.46 % of the variance. The criterion-related validity showed a significant positive correlation between the GAI-TC and STAI, with an optimal cutoff of 9/10 for detecting anxiety in older persons living in the community. The GAI-TC had good reliability and validity and can provide professionals with a tool for the early identification of anxiety among older adults.

Validation of the Chinese version of the resilience scale for the oldest-old

Background

Resilience is one of the most important variables associated with adaptive ability. The resilience scale for the oldest-old age (RSO) has been designed to measure the resilience among the oldest-old people. Originally developed in Japan, this scale has not been used in China. The objective of this study was to translate the RSO into Chinese and investigate its validity and reliability among the community's oldest-old adults aged ≥80 years.

Methods

A total of 473 oldest-old people who came from communities were recruited by convenience sampling for the assessment of construct validity using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). In addition, internal consistency reliability, test-retest reliability, face validity, and content validity were used to evaluate the psychometric characteristics of RSO.

Results

The RSO demonstrated good face validity and content validity. The content validity index of the Chinese version of the RSO was 0.890. Moreover, one factor was extracted by exploratory factor analysis, which accounted for 61.26% of the variance. The RSO had high internal consistency with a Cronbach's alpha = 0.927. The test-retest reliability was 0.785. The item-total correlations ranged from 0.752 to 0.832.

Conclusion

The results of the study indicate that the Chinese version of the RSO questionnaire has good reliability and validity and can be recommended for use by health and social service agencies as a method for assessing the resilience of the oldest-old in the community.

Computer assisted identification of stress, anxiety, depression (SAD) in students: A state-of-the-art review

Stress, depression, and anxiety are a person's physiological states that emerge from various body features such as speech, body language, eye contact, facial expression, etc. Physiological emotion is a part of human life and is associated with psychological activities. Sad emotion is relatable to negative thoughts and recognized in three stages containing stress, anxiety, and depression. These stages of Physiological emotion show various common and distinguished symptoms. The present study explores stress, depression, and anxiety symptoms in student life. The study reviews the psychological features generated through various body parts to identify psychological activities. Environmental factors, including a daily routine, greatly trigger psychological activities. The psychological disorder may affect mental and physical health adversely. The correct recognition of such disorder is expensive and time-consuming as it requires accurate datasets of symptoms. In the present study, an attempt has been made to investigate the effectiveness of computerized automated techniques that include machine learning algorithms for identifying stress, anxiety, and depression mental disorder. The proposed paper reviews the machine learning-based algorithms applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. During the review process, the proposed study found that artificial intelligence and machine learning techniques are well recommended and widely utilized in most of the existing literature for measuring psychological disorders. The various machine learning-based algorithms are applied over datasets containing questionnaires, audio, video, etc., to recognize sad details. There has been continuous monitoring for the body symptoms established in the various existing literature to identify psychological states. The present review reveals the study of excellence and competence of machine learning techniques in detecting psychological disorders' stress, depression, and anxiety parameters. This paper shows a systematic review of some existing computer vision-based models with their merits and demerits.