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Breaking the blue wall of silence: risk factors for experiencing police sexual misconduct among female offenders.

AbstractOBJECTIVES:
We assessed the prevalence of and risk factors for trading sex with a police officer among women recruited from drug courts in St Louis, Missouri.
METHODS:
In 2005 to 2008, we recruited women into an HIV intervention study, which surveyed participants about multiple sociodemographic, lifestyle, and risk factors. Regression analyses assessed risk factors for trading sex, a form of police sexual misconduct (PSM).
RESULTS:
Of the 318 participants, 78 (25%) reported a lifetime history of PSM. Among women who experienced PSM, 96% had sex with an officer on duty, 77% had repeated exchanges, 31% reported rape by an officer, and 54% were offered favors by officers in exchange for sex; 87% said officers kept their promise. Only 51% of these respondents always used a condom with an officer. Multivariable models identified 4 or more arrests (adjusted odds ratio [AOR] = 2.8; 95% confidence interval [CI] = 1.29, 5.97), adult antisocial personality (AOR = 9.0; 95% CI = 2.08, 38.79), and lifetime comorbid cocaine and opiate use (AOR = 2.9 [1.62, 5.20]) as risk factors; employment (AOR = 0.4; 95% CI = 0.22, 0.77) lowered the risk of PSM.
CONCLUSIONS:
Community-based interventions are critical to reduce risk of abuse of vulnerable women by police officers charged with protecting communities.
AuthorsLinda B Cottler, Catina C O'Leary, Katelin B Nickel, Jennifer M Reingle, Daniel Isom
JournalAmerican journal of public health (Am J Public Health) Vol. 104 Issue 2 Pg. 338-44 (Feb 2014) ISSN: 1541-0048 [Electronic] United States
PMID24328629 (Publication Type: Journal Article, Research Support, N.I.H., Extramural)
Topics
  • Adult
  • Condoms (statistics & numerical data)
  • Female
  • Humans
  • Life Style
  • Mental Health
  • Middle Aged
  • Police (statistics & numerical data)
  • Prevalence
  • Prisoners (statistics & numerical data)
  • Rape (statistics & numerical data)
  • Risk Factors
  • Sexual Behavior (statistics & numerical data)
  • Socioeconomic Factors
  • Substance-Related Disorders (epidemiology)
  • Vulnerable Populations (statistics & numerical data)

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