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Person

Suzanne Poynton

Report

​The effect of lockout and last drinks laws on non-domestic assaults in Sydney: an update to September 2016


Aims: To assess the longer-term effects of the 2014 NSW liquor law reforms on levels of violence in the inner Sydney area. Method: Interrupted time series models were used to examine the effects of the legislative reforms introduced in January 2014. Police recorded non-domestic assaults were analysed over the period January 2009 and September 2016...
Report

Intimate partner violence against women in Australia: related factors and help-seeking behaviours


Aim: To determine which factors were associated with (1) female experiences of intimate partner violence (IPV), (2) female reporting of physical or sexual assault by an intimate partner to the police and (3) females seeking help and support after experiencing IPV.
Report

Breach rate of Apprehended Domestic Violence Orders in NSW


Aim: To estimate the proportion of ADVOs breached and identify factors associated with a breach of a final order. Method: Details of all ADVOs granted between 1 July 2013 and 30 June 2014 (inclusive) were extracted from the NSW COPS database and linked to breach ADVO incidents occurring after 1 July 2013 and before 30...
Evaluation

The NSW Rolling List Court evaluation: preliminary report


Aim: To determine whether greater efficiency can be achieved through application of the Rolling List Court (RLC) model to NSW District Criminal Court matters. Method: A non-blinded randomised trial was initiated in which eligible District Criminal Court matters were randomly assigned, after committal, either to the RLC or to the general court list. Each matter...
Report

The revised group risk assessment model (GRAM 2): assessing risk of reoffending among adults given non-custodial sanctions


Of 81,199 adult offenders, 26% reoffended within two years of the index appearance. AimTo re-examine the Group Risk Assessment Model (GRAM) for predicting reoffending in adults given non-custodial sentences and to assess the accuracy of the model.

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