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Crime-scene and offender characteristics in conventional and nonconventional stranger homicides committed by male offenders in Sweden.

Sara RodreJoakim SturupThomas Masterman
Published in: Medicine, science, and the law (2024)
In Sweden, from 1990 to 2013, most homicides occurred between family members, friends or acquaintances: the annual rate of incidents between unacquainted offenders and victims ranged between 8% and 13%. In the majority of these "stranger homicides," three common motives, as defined by the precipitating event, could be identified: homicides resulting from a spontaneous altercation; homicides committed in the context of a robbery or burglary; and homicides committed in the context of a gangland conflict. The remaining minority-with uncommon or indiscernible motives-could, nonetheless, be categorized according to their nonconventional distinguishing feature: homicides characterized by the offender's ostensibly mentally aberrant behavior; homicides committed in the context of a hate offense or politically motivated offense; homicides committed in the context of a sexual offense; and homicides committed in the context of a mass killing or series of homicides. In this registry-based study of 224 incidents, "conventional" stranger homicides, defined by their commonplace motive, were compared with "nonconventional" stranger homicides, defined by their lack of such motive. The former were more often committed with an accomplice, against a male victim, whereas the latter were more often committed in a public place, after contact initiated by the offender. In the latter, offenders were less often intoxicated at the time of the offense and more often adjudged to suffer from a severe mental disorder. The subcategory of nonconventional stranger homicides characterized by the offender's ostensibly mentally aberrant behavior corresponded largely to both the archetypal stranger-homicide construct and the popular notion "act of madness."
Keyphrases
  • healthcare
  • early onset
  • quality improvement
  • neural network
  • electronic health record