scholarly journals Konseling Traumatik Untuk Menangani Gangguan Kesehatan Mental Trauma Pada Korban Pelecehan Seksual

2021 ◽  
Vol 5 (1) ◽  
pp. 29-41
Author(s):  
Ning Sasi Awaliyah ◽  
Ulin Nihayah ◽  
Khozaainatul Muna
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Child Abuse ◽  
Sexual Harassment ◽  
Large Impact ◽  
Literature Research ◽  
Mental Trauma ◽  
And Behavior

A person's mental health is affected by events in life that leave a large impact on a person's personality and behavior. These events can include domestic violence, child abuse, or long-term stress. The literature research combines the literature studies found by the authors. The results showed; 1) The victim is very traumatized by the sexual harassment incident and must be accompanied by a counselor to relax her mind. 2) implications of handling the victim, with several conditions experienced by the victim after treatment, including; a) feel relieved and already want to talk to other people and family b). feel have the spirit of life c). his condition is more stable because he feels comfortable after receiving therapy from the counselor

scholarly journals CHILD ABUSE AND ADULT MENTAL HEALTH: DOES GENDER MATTER?

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S935-S935
Author(s):  
Patricia M Morton ◽  
Blakelee Kemp ◽  
Frass Ahmed
Keyword(s):  
Mental Health ◽  
Child Abuse ◽  
Emotional Abuse ◽  
The United States ◽  
Future Research ◽  
Depression And Anxiety ◽  
Adult Mental Health ◽  
Maternal Abuse ◽  
Parental Abuse

Abstract Numerous studies have demonstrated that child abuse is associated with poor adult mental health, but few have investigated the extent to which the frequency of different types of abuse increase mental health conditions, especially at the nexus of gender. The present study examines whether parental abuse frequency and abuse perpetrator have distinct effects for men and women on three mental health outcomes—depressive symptoms, generalized anxiety, and global self-reported mental health. Data came from three waves of the National Survey of Midlife Development in the United States (MIDUS), comprising a baseline sample of 3,032 adults aged 25-74. Estimating a series of mixed effects models revealed that maternal abuse and frequent abuse during childhood were associated with poorer adult mental health during our 20-year observation period, net of childhood and adult risk factors. Specifically, maternal emotional abuse raised the risk of depression, anxiety, and lower self-rated mental health, and was more strongly associated with depression and anxiety for women than men. Compared to adults who did not experience parental abuse during childhood, adults who experienced frequent emotional and physical abuse by either parent were more likely to experience depression and anxiety and report lower ratings of mental health in adulthood. Frequent child abuse was more strongly associated with anxiety for women than men. These results demonstrate that gender differences in adult mental health have early-life antecedents. Future research investigating the long-term mental health consequences of child abuse should consider the type and magnitude of abuse as well as the perpetrator.

Child abuse in religiously-affiliated institutions: Long-term impact on men's mental health

2006 ◽  
Vol 30 (2) ◽  
pp. 205-212 ◽  
Author(s):  
David A. Wolfe ◽  
Karen J. Francis ◽  
Anna-Lee Straatman
Keyword(s):  
Mental Health ◽  
Child Abuse ◽  
Religiously Affiliated ◽  
Long Term Impact

scholarly journals CHILD ABUSE AND ADULT MENTAL HEALTH: DOES GENDER MATTER?

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S934-S934
Author(s):  
Patricia M Morton ◽  
Blakelee Kemp ◽  
Frass Ahmed
Keyword(s):  
Mental Health ◽  
Child Abuse ◽  
Emotional Abuse ◽  
The United States ◽  
Future Research ◽  
Depression And Anxiety ◽  
Adult Mental Health ◽  
Maternal Abuse ◽  
Parental Abuse

Abstract Numerous studies have demonstrated that child abuse is associated with poor adult mental health, but few have investigated the extent to which the frequency of different types of abuse increase mental health conditions, especially at the nexus of gender. The present study examines whether parental abuse frequency and abuse perpetrator have distinct effects for men and women on three mental health outcomes—depressive symptoms, generalized anxiety, and global self-reported mental health. Data came from three waves of the National Survey of Midlife Development in the United States (MIDUS), comprising a baseline sample of 3,032 adults aged 25-74. Estimating a series of mixed effects models revealed that maternal abuse and frequent abuse during childhood were associated with poorer adult mental health during our 20-year observation period, net of childhood and adult risk factors. Specifically, maternal emotional abuse raised the risk of depression, anxiety, and lower self-rated mental health, and was more strongly associated with depression and anxiety for women than men. Compared to adults who did not experience parental abuse during childhood, adults who experienced frequent emotional and physical abuse by either parent were more likely to experience depression and anxiety and report lower ratings of mental health in adulthood. Frequent child abuse was more strongly associated with anxiety for women than men. These results demonstrate that gender differences in adult mental health have early-life antecedents. Future research investigating the long-term mental health consequences of child abuse should consider the type and magnitude of abuse as well as the perpetrator.

scholarly journals Utilizing Text Mining, Data Linkage and Deep Learning in Police and Health Records to Predict Future Offenses in Family and Domestic Violence

2021 ◽  
Vol 3 ◽  
Author(s):  
George Karystianis ◽  
Rina Carines Cabral ◽  
Soyeon Caren Han ◽  
Josiah Poon ◽  
Tony Butler
Keyword(s):  
Mental Health ◽  
Domestic Violence ◽  
Deep Learning ◽  
Text Mining ◽  
Binary Classification ◽  
Contextual Information ◽  
Fine Tuning ◽  
Hands On ◽  
Mental Trauma ◽  
Transformer Model

Family and Domestic violence (FDV) is a global problem with significant social, economic, and health consequences for victims including increased health care costs, mental trauma, and social stigmatization. In Australia, the estimated annual cost of FDV is $22 billion, with one woman being murdered by a current or former partner every week. Despite this, tools that can predict future FDV based on the features of the person of interest (POI) and victim are lacking. The New South Wales Police Force attends thousands of FDV events each year and records details as fixed fields (e.g., demographic information for individuals involved in the event) and as text narratives which describe abuse types, victim injuries, threats, including the mental health status for POIs and victims. This information within the narratives is mostly untapped for research and reporting purposes. After applying a text mining methodology to extract information from 492,393 FDV event narratives (abuse types, victim injuries, mental illness mentions), we linked these characteristics with the respective fixed fields and with actual mental health diagnoses obtained from the NSW Ministry of Health for the same cohort to form a comprehensive FDV dataset. These data were input into five deep learning models (MLP, LSTM, Bi-LSTM, Bi-GRU, BERT) to predict three FDV offense types (“hands-on,” “hands-off,” “Apprehended Domestic Violence Order (ADVO) breach”). The transformer model with BERT embeddings returned the best performance (69.00% accuracy; 66.76% ROC) for “ADVO breach” in a multilabel classification setup while the binary classification setup generated similar results. “Hands-off” offenses proved the hardest offense type to predict (60.72% accuracy; 57.86% ROC using BERT) but showed potential to improve with fine-tuning of binary classification setups. “Hands-on” offenses benefitted least from the contextual information gained through BERT embeddings in which MLP with categorical embeddings outperformed it in three out of four metrics (65.95% accuracy; 78.03% F1-score; 70.00% precision). The encouraging results indicate that future FDV offenses can be predicted using deep learning on a large corpus of police and health data. Incorporating additional data sources will likely increase the performance which can assist those working on FDV and law enforcement to improve outcomes and better manage FDV events.

ANALYSIS OF THE BASIC PROVISIONS OF SOCIAL AND PSYCHOLOGICAL REHABILITATION OF VICTIMS OF EMERGENCIES

2020 ◽  
Vol 11 (4) ◽  
pp. 23-30
Author(s):  
Tverezovska. N. T ◽  
Keyword(s):  
Mental Health ◽  
Social Worker ◽  
Traumatic Event ◽  
Well Being ◽  
Value Orientations ◽  
Short Term ◽  
Great Strength ◽  
Mental Trauma

The article substantiates topical issues of extreme situations. The sociopsychological consequences of catastrophes, their impact on the mental health of the population are noted. The tasks that a social worker should solve with victims of extreme situations are highlighted. It highlights the presence of a person in the organization of a traumatic event, as well as its consequences. The types of emergencies regarding their experience by a person are disclosed: shock mental trauma, which are characterized by suddenness, great strength and, as a rule, threatening life and well-being; extreme situations for short-term action, but psychologically very significant; extreme situations of long-term action affecting the basic value orientations of a person.

Therapy and prevention for mental health: What if mental diseases are mostly not brain disorders?

2019 ◽  
Vol 42 ◽  
Author(s):  
John P. A. Ioannidis
Keyword(s):  
Mental Health ◽  
Mental Disease ◽  
Brain Disorders ◽  
Social Stressors ◽  
Labor Education ◽  
Mental Diseases ◽  
Long Term Follow Up ◽  
Political Decisions

AbstractNeurobiology-based interventions for mental diseases and searches for useful biomarkers of treatment response have largely failed. Clinical trials should assess interventions related to environmental and social stressors, with long-term follow-up; social rather than biological endpoints; personalized outcomes; and suitable cluster, adaptive, and n-of-1 designs. Labor, education, financial, and other social/political decisions should be evaluated for their impacts on mental disease.

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