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Major sports events and domestic violence: A systematic review.

Kirsty ForsdikeGrant O'SullivanLeesa Hooker
Published in: Health & social care in the community (2022)
Increased rates of domestic violence (DV) have been associated with events such as public holidays, seasonal variations, disasters and economic crises. Sport is seen as gendered, exemplifying hegemonic masculinity and associated violence, with the link between sporting culture and violence against women well recognised. This paper reports on a systematic review of empirical research literature exploring the link between major sporting events and incidence of DV. We searched MEDLINE, CINAHL, PsycINFO, SPORTDiscus and Proquest Central databases from inception to December 2020 for quantitative studies examining major sports events and reports of DV using a pre-post comparison design. Study quality was assessed using the Kmet quality assessment tool. The review identified 1445 records following duplicate removal. Once screened and assessed for eligibility, 12 studies met the inclusion criteria. Results are presented qualitatively due to the heterogeneity across studies. Most studies originated in North America and the United Kingdom, used police records as their data source for measuring incidences of DV and few looked beyond the day of the sports event for recorded incidences of DV. Studies reviewed suggested that there is an association between certain major sports events and increased reporting of DV. However, studies' findings conflicted with regards to whether increases were associated with contact sports, the rivalry between competing teams, whether the events were emotionally salient and whether alcohol was a contributing factor. In conclusion, there is limited research globally. Heterogeneity and conflicting findings mean that more research is needed to understand the associations and inform community prevention/interventions to address DV.
Keyphrases
  • mental health
  • case control
  • healthcare
  • systematic review
  • single cell
  • emergency department
  • machine learning
  • quality improvement
  • tyrosine kinase
  • data analysis