The trafficking of persons around the world is a serious violation of human rights and manifestation of social injustice. It disproportionately affects women and children worldwide. Given the values of the social work profession and the prevalence of trafficking, it is essential to understand the current literature on human trafficking in social work journals. Using the PRISMA method, this systematic review (n = 94 articles) of human trafficking in social work journals found the following: more focus on sex trafficking than other forms of trafficking; a lack of a clear conceptualization and definition on the entire spectrum of trafficking; a lack of evidence-informed empirical research to inform programs, practice, and policy; and a dearth of recommendations for social work education. Specific implications for social work policy, research, practice, and education are highlighted and discussed.
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http://dx.doi.org/10.1080/23761407.2017.1415177 | DOI Listing |
Soc Work Health Care
January 2025
Faculty of Clinical Medicine, Hanoi University of Public Health, Hanoi, Vietnam.
Studies on the hospital social work workforce in global contexts remain unexplored. This study aims to describe the workforce status for hospital social work in Vietnam. This study involved 676 central, provincial, and district hospitals in Vietnam.
View Article and Find Full Text PDFArch Public Health
January 2025
Fundació Institut Universitari per a la Recerca a l'Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Gran Via de les Corts Catalanes, 587 attic., Barcelona, 08007, Spain.
Objective: To analyze the sociostructural determinants associated with mental health problems during the lockdown period among populations residing in Brazil, Chile, Ecuador, Mexico, Peru, and Spain who lived with minors or dependents, approached from a gender perspective.
Methods: A cross-sectional study was conducted in six participating countries via an adapted, self-managed online survey. People living with minors and/or dependents were selected.
BMC Health Serv Res
January 2025
Australian Centre for Health Services Innovation and Centre for Healthcare Transformation, School of Public Health and Social Work, Faculty of Health, Queensland University of Technology, Brisbane, QLD, Australia.
Background: Unwarranted clinical variation presents a major challenge in contemporary healthcare, indicating potential inequalities and inefficiencies, and unrealised potential for better outcomes. Despite an increasing focus on unwarranted clinical variation, and consideration of efforts to address this challenge, evidence-based strategies which achieve this are limited. Audit and feedback of healthcare processes (process auditing) and clinician engagement are important tools which may help to reduce unwarranted clinical variation, however their application in maternity care is yet to be thoroughly explored.
View Article and Find Full Text PDFBMC Psychiatry
January 2025
Division of Epidemiology and Social Sciences, Institute for Health and Equity, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI, 53226, USA.
Background: During adolescence, a critical developmental phase, cognitive, psychological, and social states interact with the environment to influence behaviors like decision-making and social interactions. Depressive symptoms are more prevalent in adolescents than in other age groups which may affect socio-emotional and behavioral development including academic achievement. Here, we determined the association between depression symptom severity and behavioral impairment among adolescents enrolled in secondary schools of Eastern and Central Uganda.
View Article and Find Full Text PDFSci Rep
January 2025
College of Physical Education and Health Sciences, Zhejiang Normal University, Jinhua, 321004, China.
Athlete engagement is influenced by several factors, including cohesion, passion and mental toughness. Machine learning methods are frequently employed to construct predictive models as a result of their high efficiency. In order to comprehend the effects of cohesion, passion and mental toughness on athlete engagement, this study utilizes the relevant methods of machine learning to construct a prediction model, so as to find the intrinsic connection between them.
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