title: Āio

authors: Maxwell, Te Kahautu

abstract: Whakawātea. Lyrics by Te Kahautu Maxwell. Musical score by Thomas Mitai. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group)

Waiata Tira: Aroha
Whakaeke: Haere mai rā
Mōteatea: Te Pātere a Te Turuki
Poi: Iarararararī
Waiata ā ringa: Taku Mana Māori Motuhake
Haka: Waiwharangi
Whakawātea: Āio

text: Āio Whakawātea. Lyrics by Te Kahautu Maxwell. Musical score by Thomas Mitai. Performed by Ōpōtiki Mai Tawhiti (Kapa Haka group)

Waiata Tira: Aroha
Whakaeke: Haere mai rā
Mōteatea: Te Pātere a Te Turuki
Poi: Iarararararī
Waiata ā ringa: Taku Mana Māori Motuhake
Haka: Waiwharangi
Whakawātea: Āio

year: 2020-02-06

title: Does within-treatment change on dynamic risk factors and psychopathology predict recidivism for men graduating from Tai Aroha?

authors: Tanner Dempsey, Arden

subjects: dynamic risk factors

abstract: Dynamic risk factors are modifiable aspects of justice-involved people and their environment that can change over time and are associated with criminal behaviour. Because they are considered changeable, they are therefore typically the focus of rehabilitative treatment (Bonta & Andrews, 2024). Research shows that correctional rehabilitation that targets dynamic risk factors effectively reduces recidivism, and psychometric tools assessing these factors generally show improvements during treatment. However, there is a missing link; current research lacks consistent evidence linking changes on dynamic risk factors during treatment to recidivism. Relatedly, there are few studies assessing within-treatment changes in psychopathology and how they may affect recidivism, though they are commonly assessed alongside dynamic risk factors in correctional settings. 
This study, involving 164 people with violent offence convictions who completed the Tai Aroha community treatment programme, aimed to address this missing link. A battery of tests measured pre- and post-programme included dynamic risk factor measures, the Anger Disorders Scale (ADS), Psychological Inventory of Criminal Thinking Styles (PICTS), Criminal Attitudes to Violence Scale (CAVS), and the Millon Clinical Multiaxial Inventory (MCMI-III and IV) which is designed to assess personality and clinical psychopathology. Standardised residual change scores were used to account for variance in pre-treatment scores, and Cox regression hazard ratios to test prediction of general and violent recidivism.
Most subscales for dynamic risk factor measures and the MCMI-III and IV showed substantial improvements during treatment, however, this did not translate to predicting the probability of recidivism. While several hazard ratios indicated effects in a positive direction, none were significant when adjusted for multiple comparisons, and controlling for actuarial risk did not improve predictive accuracy. There was some evidence of increased socially desirable responding which increased at the end of treatment, this included responses on the MCMI-III and IV consistent with the “Normal Quartet.” Interpretations of these findings and both theoretical and empirical implications are discussed.

text: Does within-treatment change on dynamic risk factors and psychopathology predict recidivism for men graduating from Tai Aroha? Dynamic risk factors are modifiable aspects of justice-involved people and their environment that can change over time and are associated with criminal behaviour. Because they are considered changeable, they are therefore typically the focus of rehabilitative treatment (Bonta & Andrews, 2024). Research shows that correctional rehabilitation that targets dynamic risk factors effectively reduces recidivism, and psychometric tools assessing these factors generally show improvements during treatment. However, there is a missing link; current research lacks consistent evidence linking changes on dynamic risk factors during treatment to recidivism. Relatedly, there are few studies assessing within-treatment changes in psychopathology and how they may affect recidivism, though they are commonly assessed alongside dynamic risk factors in correctional settings. 
This study, involving 164 people with violent offence convictions who completed the Tai Aroha community treatment programme, aimed to address this missing link. A battery of tests measured pre- and post-programme included dynamic risk factor measures, the Anger Disorders Scale (ADS), Psychological Inventory of Criminal Thinking Styles (PICTS), Criminal Attitudes to Violence Scale (CAVS), and the Millon Clinical Multiaxial Inventory (MCMI-III and IV) which is designed to assess personality and clinical psychopathology. Standardised residual change scores were used to account for variance in pre-treatment scores, and Cox regression hazard ratios to test prediction of general and violent recidivism.
Most subscales for dynamic risk factor measures and the MCMI-III and IV showed substantial improvements during treatment, however, this did not translate to predicting the probability of recidivism. While several hazard ratios indicated effects in a positive direction, none were significant when adjusted for multiple comparisons, and controlling for actuarial risk did not improve predictive accuracy. There was some evidence of increased socially desirable responding which increased at the end of treatment, this included responses on the MCMI-III and IV consistent with the “Normal Quartet.” Interpretations of these findings and both theoretical and empirical implications are discussed.

year: 2024-01-23

title: Genomic tools in biological invasions: Current state and future frontiers

authors: McGaughran, Angela

subjects: biological invasion

abstract: Human activities are accelerating rates of biological invasions and climate-driven range expansions globally, yet we understand little of how genomic processes facilitate the invasion process. Although most of the literature has focused on underlying phenotypic correlates of invasiveness, advances in genomic technologies are showing a strong link between genomic variation and invasion success.

Here, we consider the ability of genomic tools and technologies to: (i) inform mechanistic understanding of biological invasions; and (ii) solve real-world issues in predicting and managing biological invasions. For both, we examine the current state of the field and discuss how genomics can be leveraged in the future. In addition, we make recommendations pertinent to broader research issues, such as data sovereignty, metadata standards, collaboration, and science communication best practices that will require concerted efforts from the global invasion genomics community.

text: Genomic tools in biological invasions: Current state and future frontiers Human activities are accelerating rates of biological invasions and climate-driven range expansions globally, yet we understand little of how genomic processes facilitate the invasion process. Although most of the literature has focused on underlying phenotypic correlates of invasiveness, advances in genomic technologies are showing a strong link between genomic variation and invasion success.

Here, we consider the ability of genomic tools and technologies to: (i) inform mechanistic understanding of biological invasions; and (ii) solve real-world issues in predicting and managing biological invasions. For both, we examine the current state of the field and discuss how genomics can be leveraged in the future. In addition, we make recommendations pertinent to broader research issues, such as data sovereignty, metadata standards, collaboration, and science communication best practices that will require concerted efforts from the global invasion genomics community.

year: 2024