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  • 标题:THE USE OF ADMINISTRATIVE DATA TO STUDY THE TRIPLE P – POSITIVE PARENTING PROGRAM
  • 本地全文:下载
  • 作者:Rubab G. Arim ; Anne Guevremont ; V. Susan Dahinten
  • 期刊名称:International Journal of Child, Youth & Family Studies
  • 电子版ISSN:1920-7298
  • 出版年度:2017
  • 卷号:8
  • 期号:3/4
  • 页码:59-78
  • DOI:10.18357/ijcyfs83/4201718001
  • 语种:English
  • 出版社:University of Victoria
  • 摘要:This study is among the first Canadian population-based evaluations designed to examine associations of the Triple P – Positive Parenting Program (Triple P) for mother and child outcomes at the community level. Uniquely, this study was conducted independently of program implementation, using data collected for other purposes. Three anonymized British Columbia provincial administrative data sources were used, in addition to program data collected by administrators (Island Health). The study employed a quasi-experimental design to examine benefits of Triple P at the community level, and used sociodemographic community characteristics to match 11 target communities where Triple P was implemented with comparison communities where Triple P was not implemented. The study’s design and analyses took into account the phased-in implementation of Triple P across Vancouver Island (2004–2008), drawing on pre- and post-implementation data for all of the studied communities. Hierarchical linear modeling results showed that children living in communities where the program had been administered were more likely to have been diagnosed with conduct disorders and to have used counseling services. Program intensity was not associated with any of the child health outcomes. For mothers, higher program intensity was associated with lower odds of being diagnosed with mental health conditions. Future research should continue to demonstrate the feasibility of a population-based approach and the use of secondary data along with program data to examine community-based intervention programs.
  • 关键词:intervention; child; maternal; mental health; services; conduct disorder; anxiety; secondary data; hierarchical linear modeling
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