liu.seSearch for publications in DiVA
Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Children in Identified Sexual Images - Who Are they? Self- and Non-Self-Taken Images in the International Child Sexual Exploitation Image Database 2006-2015
Univ Edinburgh, Scotland.
Linköpings universitet, Institutionen för klinisk och experimentell medicin, Barnafrid. Linköpings universitet, Medicinska fakulteten. Region Östergötland, Närsjukvården i centrala Östergötland, Barn- och ungdomspsykiatriska kliniken.
Univ Edinburgh, Scotland.
NCA CEOP Command, England.
Vise andre og tillknytning
2018 (engelsk)Inngår i: Child Abuse Review, ISSN 0952-9136, E-ISSN 1099-0852, Vol. 27, nr 3, s. 223-238Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Child sexual abuse and exploitation material has drawn concern and legislative attention since the turn of the century, and the work to identify children in the images has been a prioritised task through international cooperation. The International Child Sexual Exploitation Image Database (ICSE DB) includes more than 8000 identified victims from nearly 50 countries. The database contains considerable important information about child abuse image crimes. The general aim of this study was to quantify the characteristics of children in identified illegal images from the UK ICSE DB (n = 687) with the subsidiary aim to describe differences between cases of self-taken images and those whose images had been taken by others. The analysis showed an increase in identified victims during the study years 2006-2015. Almost two-thirds were female, the majority were white and 44.3 per cent of images were self-taken (34.4% taken in a coercive and 9.9% in a non-coercive relationship). Since 2010, the number of self-taken images each year has exceeded more than 40 per cent of the total number of images in the database. Although self-taken images may be perceived as less worrisome, two-thirds were classified as coercive. This is an important argument in favour of continuing to investigate these cases under victim identification programmes. The general aim of this study was to quantify the characteristics of children in identified illegal images from the UK ICSE DB Key Practitioner Messages The ICSE DB includes more than 8000 identified victims and contains important information about child abuse image crimes. A majority of the identified victims were female and white children. Almost half of all images were self-taken and had been taken in a coercive relationship. Parents and practitioners need to recognise that even if a child sends sexual images these should be considered worrisome and therefore investigated further.

sted, utgiver, år, opplag, sider
WILEY , 2018. Vol. 27, nr 3, s. 223-238
Emneord [en]
sexual images; child sexual abuse and exploitation material; self-taken; children; ICSE DB
HSV kategori
Identifikatorer
URN: urn:nbn:se:liu:diva-149739DOI: 10.1002/car.2507ISI: 000436537400006OAI: oai:DiVA.org:liu-149739DiVA, id: diva2:1234368
Merknad

Funding Agencies|European Commission Safer Internet Programme [SI-2012-KEP-411207]

Tilgjengelig fra: 2018-07-24 Laget: 2018-07-24 Sist oppdatert: 2018-07-24

Open Access i DiVA

Fulltekst mangler i DiVA

Andre lenker

Forlagets fulltekst

Søk i DiVA

Av forfatter/redaktør
Jonsson, LindaSvedin, Carl Göran
Av organisasjonen
I samme tidsskrift
Child Abuse Review

Søk utenfor DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 193 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf