scholarly journals Preventing violence by intimate partners in adolescence: an integrative review

2016 ◽  
Vol 50 (1) ◽  
pp. 134-143 ◽  
Author(s):  
Rebeca Nunes Guedes De Oliveira ◽  
Rafaela Gessner ◽  
Bianca de Cássia Alvarez Brancaglioni ◽  
Rosa Maria Godoy Serpa da Fonseca ◽  
Emiko Yoshikawa Egry

Abstract OBJECTIVE To analyze the scientific literature on preventing intimate partner violence among adolescents in the field of health based on gender and generational categories. METHOD This was an integrative review. We searched for articles using LILACS, PubMed/MEDLINE, and SciELO databases. RESULTS Thirty articles were selected. The results indicate that most studies assessed interventions conducted by programs for intimate partner violence prevention. These studies adopted quantitative methods, and most were in the area of nursing, psychology, and medicine. Furthermore, most research contexts involved schools, followed by households, a hospital, a health center, and an indigenous tribe. CONCLUSION The analyses were not conducted from a gender- and generation-based perspective. Instead, the scientific literature was based on positivist research models, intimately connected to the classic public healthcare model and centered on a singular dimension.

10.2196/15347 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e15347
Author(s):  
Christopher Michael Homan ◽  
J Nicolas Schrading ◽  
Raymond W Ptucha ◽  
Catherine Cerulli ◽  
Cecilia Ovesdotter Alm

Background Social media is a rich, virtually untapped source of data on the dynamics of intimate partner violence, one that is both global in scale and intimate in detail. Objective The aim of this study is to use machine learning and other computational methods to analyze social media data for the reasons victims give for staying in or leaving abusive relationships. Methods Human annotation, part-of-speech tagging, and machine learning predictive models, including support vector machines, were used on a Twitter data set of 8767 #WhyIStayed and #WhyILeft tweets each. Results Our methods explored whether we can analyze micronarratives that include details about victims, abusers, and other stakeholders, the actions that constitute abuse, and how the stakeholders respond. Conclusions Our findings are consistent across various machine learning methods, which correspond to observations in the clinical literature, and affirm the relevance of natural language processing and machine learning for exploring issues of societal importance in social media.


2019 ◽  
pp. 088626051983942 ◽  
Author(s):  
Susi McGhee ◽  
Binita Shrestha ◽  
Gemma Ferguson ◽  
Prabin Nanicha Shrestha ◽  
Irina Bergenfeld ◽  
...  

2019 ◽  
Vol 6 (4) ◽  
pp. 259-266 ◽  
Author(s):  
Lusajo J. Kajula ◽  
Mrema N. Kilonzo ◽  
Donaldson F. Conserve ◽  
Gema Mwikoko ◽  
Deus Kajuna ◽  
...  

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