BACKGROUND

In response to the need to provide for mental health services to address gender-related concerns in a higher education institute, the University of the Philippines (UP) Manila Center for Gender and Women Studies (CGWS) commissioned a project to formulate a framework for the increasing volume of referrals.

METHODOLOGY

A mixed methods study was done in order to gather data to create a responsive and practical mental health care service provision framework with and for service providers and service users in the university. An online survey (N=135), focus group discussion, key informant interviews, and a round table discussion were conducted, with constituents of the university recruited through purposive sampling.

RESULTS

A stepped-care model was proposed, consisting of: 1. Preventive Well-Being Resources, 2. Supportive Well-Being Interventions and Initial Screening Resources, 3. Structured Interventions, and 4. Interventions for Severe Mental Health Problems.

CONCLUSION

The framework formulated in collaboration with service providers and service users in the university addresses the goals of optimizing existing resources and enhancing service provision. Implementation and evaluation of this framework, as well as further information regarding the target population and their use of this model, are proposed avenues for further research.

Human ; Gender ; Gender Identity ; Sexual Harassment ; Mental Health ; Mental Health Services ; Lgbtq ; Sexual And Gender Minorities ; Psychiatry ; Psychology

BACKGROUND

Community Health Workers (CHWs) play a vital role in addressing community healthcare needs, yet little is known about their psychosocial status and job satisfaction. This study aimed to describe the psychosocial status and job satisfaction of CHWs, and examine the factors associated with their job satisfaction.

METHODOLOGY

A cross-sectional study was conducted among 440 CHWs aged 25-60 years working in urban and rural areas of Batangas, Philippines. Linear regression models were used to examine the association between psychosocial factors and job satisfaction. Other factors associated with job satisfaction were also examined.

RESULTS

Despite 90.0% of CHWs reporting high perceived stress and 52.1% experiencing high depressive symptoms, they demonstrated relatively high job satisfaction (mean [standard deviation]= 80.05 [17.56]; range= 0-100) and high perceived social support (mean [standard deviation]= 25.09 [2.93]; range= 10-30). Among psychosocial factors, only perceived social support was associated with job satisfaction (unstandardized beta [B] 0.93; 95% confidence interval [CI] 0.44, 1.41). Other factors associated with job satisfaction include fixed working hours (B 4.71; 95% CI 0.49, 8.94), work amenities (B 7.37; 95% Cl 0.03, 14.72), ≥21 years of work experience (B 5.64; 95% CI 0.35, 10.93), and working in rural areas (B 5.88; 95% CI 2.77, 8.99).

CONCLUSION

Psychological factors such as perceived stress and depressive symptoms were not found to be associated with job satisfaction among CHWs. However, factors such as greater perceived social support, fixed working hours, work amenities, longer work experience, and working in rural areas were identified as contributors to higher levels of job satisfaction among CHWs.

Human ; Community Health Workers ; Job Satisfaction ; Philippines ; Psychological Factors ; Psychology ; Working Conditions

BACKGROUND: Menopausal disorders include obscure symptomatology that greatly reduce work productivity among female workers. Quantifying the impact of menopause-related symptoms on work productivity is very difficult because no such guidelines exist to date. We aimed to develop a scale of overall health status for working women in the perimenopausal period. METHODS: In September, 2021, we conducted an Internet web survey which included 3,645 female workers aged 45-56 years in perimenopausal period. We asked the participants to answer 76 items relevant to menopausal symptomatology, that were created for this study and performed exploratory and confirmatory factor analyses for the scale development. Cronbach's alpha, receiver operating characteristic analysis, and logistic regression analysis were used to verify the developed scale. RESULTS: Approximately 85% participants did not have menstruation or disrupted cycles. Explanatory factor analysis using the maximum likelihood method and Promax rotation identified 21 items with a four-factor structure: psychological symptoms (8 items, α = 0.96); physiological symptoms (6 items, alpha = 0.87); sleep difficulty (4 items, alpha = 0.92); human relationship (3 items, alpha = 0.92). Confirmatory factor analyses found excellent model fit for the four-factor model (RMSR = 0.079; TLI = 0.929; CFI = 0.938). Criterion and concurrent validity were confirmed with high correlation coefficients between each of the four factors, previously validated menopausal symptom questionnaire, and Copenhagen Burnout Inventory scales, respectively (all ps < 0.0001). The developed scale was able to predict absenteeism with 78% sensitivity, 58% specificity, and an AUC of 0.727 (95%CI: 0.696-0.757). Higher scores of each factor as well as total score of the scale were more likely to be associated with work absence experience due to menopause-related symptoms even after adjusting for Copenhagen Burnout Inventory subscales (all ps < 0.0001). CONCLUSION We found that the developed scale has high validity and reliability and could be a significant indicator of absenteeism for working women in perimenopausal period.
Humans ; Female ; Perimenopause ; Reproducibility of Results ; Menopause/psychology* ; Workplace ; Surveys and Questionnaires ; Psychometrics

The increased usage of digital platforms for communication and networking, particularly after the pandemic has caused concern about exposure to bullying and sexual harassment, particularly among young children and adolescents. Cyberbullying (CB) and cyber sexual harassment (CSH), although considered less harmful by many, may cause graver psychological manifestations than their physical forms, due to their potential for wider reach, easier access to private and sensitive information, ability to conceal perpetrator identity and continuous exposure of the victim to the harmful acts. Among the various characteristics, there were differences between age groups and gender, as well as varied psychological and behavioral features among victims and perpetrators which included low self-esteem, low academic performance and interestingly, some victims who themselves, later engage in perpetrating CB on others. The strategies for the prevention of CB and CSH are similar to that of the traditional forms, which predominantly include raising awareness among students, teachers and parents. This article reviews the characteristics of CB and CSH and discusses the limitations in existing laws and statutes in combating CB and CSH while highlighting the need for improving the policies and guidelines on CB and CSH among educational institutions and workplaces.
Psychology, Adolescent ; Gender-Based Violence

Background: Suicide among adolescents is a critical global health problem. Identifying risk factors for suicide in adolescents is crucial because it is one of the most severe mental health issues and can result in loss of life. Risk factors serve as indicators that have the potential to bring life to an end. However, people around adolescents often display indifference and even tend to overlook the suicide risk factors experienced by them. Objective: This study aimed to explore the risk factors for suicide in adolescents in Indonesia. Methods: This study used qualitative descriptive research design conducted at State Vocational High Schools (SMKN) and Puskesmas. Data collection was done through Focus Group Discussion (FGD) of 10 students, and in-depth interviews of eight participants (two parents of adolescents who attempted suicide, two guidance counseling teachers, two adolescents who attempted suicide, and two mental nurses) The data were analyzed using thematic analysis. Results: The risk factors for suicide experienced by adolescents are biological, psychological, and social factors. These risk factors for suicide are stressors that contribute to adolescents engaging in suicidal behavior. Identifying the risk factors experienced by adolescents is crucial for suicide prevention. Conclusion The risk factors that lead to suicide in adolescents encompass biological, psychological, and social factors. A thorough understanding of suicide among parents, teachers, and peers can significantly assist in implementing suitable prevention measures and interventions for adolescent suicide.
Adolescent ; Risk Factors ; Biological Factors ; Psychology ; Social Factors ; Suicide

OBJECTIVE: This study aimed to evaluate the effects of a mindfulness-based psychosomatic intervention on depression, anxiety, fear of childbirth (FOC), and life satisfaction of pregnant women in China. METHODS: Women experiencing first-time pregnancy ( n = 104) were randomly allocated to the intervention group or a parallel active control group. We collected data at baseline (T0), post-intervention (T1), 3 days after delivery (T2), and 42 days after delivery (T3). The participants completed questionnaires for the assessment of the levels of depression, anxiety, FOC, life satisfaction, and mindfulness. Differences between the two groups and changes within the same group were analyzed at four time points using repeated-measures analysis of variance. RESULTS: Compared with the active control group, the intervention group reported lower depression levels at T2 ( P = 0.038) and T3 ( P = 0.013); reduced anxiety at T1 ( P = 0.001) and T2 ( P = 0.003); reduced FOC at T1 ( P < 0.001) and T2 ( P = 0.04); increased life satisfaction at T1 ( P < 0.001) and T3 ( P = 0.015); and increased mindfulness at T1 ( P = 0.01) and T2 ( P = 0.006). CONCLUSION The mindfulness-based psychosomatic intervention effectively increased life satisfaction and reduced perinatal depression, anxiety, and FOC.
Humans ; Pregnancy ; Female ; Mental Health ; Mindfulness ; Pregnant Women/psychology* ; Anxiety/prevention & control* ; China ; Depression/prevention & control*

Behavioral and psychological symptoms of dementia (BPSD) are common in the patients with dementia.Creative arts therapies (CAT) are one of the safe and effective non-pharmacological interventions for BPSD.This paper elaborates on the therapeutic effects of four common CAT,including art therapy,music therapy,dance therapy,and drama therapy,on BPSD.Despite the shortcomings,CAT offer a new gateway for the safe and noninvasive treatment of BPSD.
Humans ; Art Therapy ; Music Therapy ; Dementia/psychology*

Macaque is a common animal model in drug safety assessment. Its behavior reflects its health condition before and after drug administration, which can effectively reveal the side effects of drugs. At present, researchers usually rely on artificial methods to observe the behavior of macaque, which cannot achieve uninterrupted 24-hour monitoring. Therefore, it is urgent to develop a system to realize 24-hour observation and recognition of macaque behavior. In order to solve this problem, this paper constructs a video dataset containing nine kinds of macaque behaviors (MBVD-9), and proposes a network called Transformer-augmented SlowFast for macaque behavior recognition (TAS-MBR) based on this dataset. Specifically, the TAS-MBR network converts the red, green and blue (RGB) color mode frame input by its fast branches into residual frames on the basis of SlowFast network and introduces the Transformer module after the convolution operation to obtain sports information more effectively. The results show that the average classification accuracy of TAS-MBR network for macaque behavior is 94.53%, which is significantly improved compared with the original SlowFast network, proving the effectiveness and superiority of the proposed method in macaque behavior recognition. This work provides a new idea for the continuous observation and recognition of the behavior of macaque, and lays the technical foundation for the calculation of monkey behaviors before and after medication in drug safety evaluation.
Animals ; Electric Power Supplies ; Macaca ; Recognition, Psychology

The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Humans ; Mental Disorders/diagnosis* ; Alzheimer Disease/diagnosis* ; Brain Injuries ; Electroencephalography ; Recognition, Psychology

Recently, deep learning has achieved impressive results in medical image tasks. However, this method usually requires large-scale annotated data, and medical images are expensive to annotate, so it is a challenge to learn efficiently from the limited annotated data. Currently, the two commonly used methods are transfer learning and self-supervised learning. However, these two methods have been little studied in multimodal medical images, so this study proposes a contrastive learning method for multimodal medical images. The method takes images of different modalities of the same patient as positive samples, which effectively increases the number of positive samples in the training process and helps the model to fully learn the similarities and differences of lesions on images of different modalities, thus improving the model's understanding of medical images and diagnostic accuracy. The commonly used data augmentation methods are not suitable for multimodal images, so this paper proposes a domain adaptive denormalization method to transform the source domain images with the help of statistical information of the target domain. In this study, the method is validated with two different multimodal medical image classification tasks: in the microvascular infiltration recognition task, the method achieves an accuracy of (74.79 ± 0.74)% and an F1 score of (78.37 ± 1.94)%, which are improved as compared with other conventional learning methods; for the brain tumor pathology grading task, the method also achieves significant improvements. The results show that the method achieves good results on multimodal medical images and can provide a reference solution for pre-training multimodal medical images.
Humans ; Algorithms ; Brain/diagnostic imaging* ; Brain Neoplasms/diagnostic imaging* ; Recognition, Psychology