This study aimed to find associated patterns by association rule mining and propose a prediction model using ensemble learning methods of high levels of trauma items affecting depression and suicide among primary school students in Thai rural extended opportunity schools. Our proposed methods were different from others that have analysed the relationship of high life trauma leading to depression and suicide by using statistical analysis. We found strongly associated patterns and effects among primary students’ trauma, depression, and suicide. The trauma of psychological abuse and neglect may result in suicide, whereas psychological abuse, neglect, and the experience of self-harm are also likely to result in the increased severity of traumatic events in life. The trauma of physical and sexual abuse, neglect, helplessness, feeling worthless, being weak, and self-harm were associated with depression. Our research discovered new knowledge that the risk of suicide arises from two extreme types of trauma: when children’s safety is frequently threatened and the family communicates frequently using rude or abusive words; these traumas may not merely correlate with depression but may ultimately result in suicide. Moreover, this study discovered 7 highly important trauma items and 4 suicide items for predicting depression and suicide using the Random Forest technique. We found that the Random Forest technique performed well in predicting depression and suicide. The predicted depression results show that the overall accuracy was 85.84%, precision was 89.33%, and recall was 75.28%. The predicted suicide results show that the overall accuracy was 91.28%, precision was 89.05%, and recall was 84.72%. From these results, we identified high life trauma affecting depression and suicide, which are very beneficial to practitioners to use in preliminary screening. In addition, those involved need to be aware and attentive in counselling these people with these symptoms in time. Doi: 10.28991/ESJ-2022-06-04-02 Full Text: PDF

Background: Psychiatric problems have been a growing and significant public health challenge. Community-based psychiatric services have been shown to contribute to the improvement of health and social-related outcomes, but with limited specialists in this field. Consequently, Thailand has integrated these services into the primary care system delivered by community nurses working in primary care units (PCUs). Aim: To describe the experiences of psychiatric patients in receiving services provided by non-specialist community nurses. Method: A phenomenological approach was used. Eleven psychiatric patients with 10 major depressive disorders and 1 with schizophrenia were interviewed. The results were evaluated using thematic content analysis. Results: The results revealed two main themes as barriers and facilitators experienced in receiving services from community nurses. Conclusion: Community-based services should be concerned about developing psychiatric nursing competency for community nurses to extend basic services to patients in communities and to assist family members.

This paper proposes a proactive method to detect the clinical depression affected person from post and behavior data from Facebook, called text-based and behavior-based models, respectively. For a text-based model, the words that make up the posts are separated and converted into vectors of terms. A machine learning classification applies the term frequency- inverse document frequency technique to identify important or rare words in the posts. For the behavior-based model, the statistical values of the behavioral data were designed to capture depressive symptoms. The results showed that the behavior-based model was able to detect depressive symptoms better than the text-based model. Regarding performance, a detection model using behaviors yields significantly higher F1 scores than those using words in the post. The K-nearest neighbor (KNN) classifier is the best model with the highest F1 score of 1.0, while the highest F1 score of the behavior-based model is 0.88. An analysis of the predominant features influencing depression signifies that posted messages could detect feelings of self-hatred and suicidal thoughts. At the same time, behavioral manifestations identified depressed people who manifested as restlessness, insomnia, decreased concentration.

Severe depression with psychotic symptoms and suicidal behaviors is a critical mental health condition requiring comprehensive care. While clinical interventions are necessary, less is known about the lived experiences of individuals who recover from such complex states. This study explores the lived experiences of recovery among individuals diagnosed with major depressive disorder with severe depression, psychotic symptoms, and suicidal ideation or suicide attempts, focusing on how they found meaning in their journey and maintained recovery over time. A phenomenological approach was employed. In-depth interviews were conducted with nine individuals who had experienced severe depression with psychotic symptoms and suicidal behaviors, received psychiatric treatment, and later achieved recovery. Data were analyzed using interpretive phenomenological analysis. Participants described recovery as a deeply personal and transformative journey. Three core themes emerged: (1) understanding and reframing internal experiences, (2) drawing strength from therapy, relationships, and self-care, and (3) gradually regaining agency, identity, and meaning in life. Recovery from severe depression with psychotic symptoms and suicidal behaviors is possible. Mental health nurses and professionals play a vital role in supporting this journey through person-centered, holistic, and empowering care approaches.

Lesbian, Gay, Bisexual, Transgender, Queer/Questioning, and other (LGBTQ+) adolescents face a higher risk of depression than their heterosexual and cisgender peers, necessitating targeted mental health interventions. This study examined the prevalence of depression and its associated factors among LGBTQ+ adolescents in Southern Thailand, using the Minority Stress Model as a framework to explain how chronic stressors contribute to mental health disparities in this population. Specifically, it explored the association of distal stressors such as bullying, discrimination, and family relationships, as well as proximal stressors including self-esteem and gender identity confidence, with depression. A cross-sectional study was conducted among 958 adolescents aged 13–18 who openly identified as LGBTQ+. Participants were recruited from four secondary schools through stratified random sampling from the Secondary Educational Service Area Office 11. Data were collected using the Patient Health Questionnaire for Adolescents, Rosenberg’s Self-Esteem Scale, the Experience of Discrimination Scale, and the Revised Olweus Bully/Victim Questionnaire. Analyses included descriptive statistics, Spearman’s rank correlation test, and Ordinal logistic regression. Results showed that 84.8% of participants experienced depressive symptoms ranging from mild to severe. Low self-esteem emerged as a significant risk factor, while supportive family relationships served as a protective factor. These findings underscore the high prevalence of depression in this population and the need to strengthen self-esteem, promote family acceptance, and implement inclusive school and community policies. Culturally sensitive, trauma-informed nursing interventions can reduce mental health disparities and promote resilience among LGBTQ+ adolescents in Thailand and similar contexts by fostering self-esteem, strengthening family support, and promoting inclusivity.