An Investigation of How Change in Dynamic Risk and Protective Factors Affects the Prediction of Imminent Criminal Recidivism

<p>Dynamic risk and protective factors are changeable, psychosocial variables associated with an increased or decreased likelihood of future criminal behaviour. These variables have an important role in correctional psychology. In particular, they are increasingly central to the management and supervision of individuals released from prison. The changeable nature of these variables means that, with frequent reassessment, the likelihood of recidivism can be monitored during the release period, and intervention can be more carefully targeted to an individual’s needs. However, research has yet to clearly demonstrate that reassessment of dynamic risk and protective factors can accurately track the likelihood of recidivism over time. Further, relatively little is known about how these variables change over time, and how change is associated with recidivism. This thesis set out to investigate whether reassessment of a dynamic risk assessment tool—the Dynamic Risk Assessment for Offender Re-entry (DRAOR; Serin, 2007; Serin, Mailloux, & Wilson, 2012)—would enhance the prediction of imminent recidivism among a large sample of high-risk men (n = 966) released from prison on parole in New Zealand. The analyses addressing this question were divided into three primary sections: 1) an investigation of whether a single proximal assessment was a more accurate predictor of imminent recidivism than a single baseline assessment completed shortly after release; 2) an investigation of whether a single proximal assessment was a more accurate predictor of recidivism than a series of aggregated measures across multiple time points, and; 3) an investigation of whether several different measures of intra-individual change demonstrated incremental predictive validity over the most proximal assessment. This approach represented a replication and extension of the framework set out by Lloyd (2015) in a recent thesis for testing whether reassessment of dynamic risk and protective factors enhances the prediction of imminent recidivism. Across all three sections, results provided consistent evidence that the most proximal assessment was the most accurate predictor of imminent recidivism. The most proximal assessment was a significantly more accurate predictor than a baseline assessment, and neither aggregation nor measures of intra-individual change clearly improved predictive accuracy. These results highlight the importance of reassessment for monitoring changes in the likelihood of recidivism over time and have important implications for community correctional agencies who are tasked with managing individuals released from prison, particularly those deemed to be the highest risk of recidivism. The results also have theoretical implications for the concepts of dynamic risk and protective factors and their role in the process leading to recidivism. A better understanding of the recidivism process should lead to intervention strategies that are more effective at reducing recidivism.</p>

An Investigation of How Change in Dynamic Risk and Protective Factors Affects the Prediction of Imminent Criminal Recidivism

<p>Dynamic risk and protective factors are changeable, psychosocial variables associated with an increased or decreased likelihood of future criminal behaviour. These variables have an important role in correctional psychology. In particular, they are increasingly central to the management and supervision of individuals released from prison. The changeable nature of these variables means that, with frequent reassessment, the likelihood of recidivism can be monitored during the release period, and intervention can be more carefully targeted to an individual’s needs. However, research has yet to clearly demonstrate that reassessment of dynamic risk and protective factors can accurately track the likelihood of recidivism over time. Further, relatively little is known about how these variables change over time, and how change is associated with recidivism. This thesis set out to investigate whether reassessment of a dynamic risk assessment tool—the Dynamic Risk Assessment for Offender Re-entry (DRAOR; Serin, 2007; Serin, Mailloux, & Wilson, 2012)—would enhance the prediction of imminent recidivism among a large sample of high-risk men (n = 966) released from prison on parole in New Zealand. The analyses addressing this question were divided into three primary sections: 1) an investigation of whether a single proximal assessment was a more accurate predictor of imminent recidivism than a single baseline assessment completed shortly after release; 2) an investigation of whether a single proximal assessment was a more accurate predictor of recidivism than a series of aggregated measures across multiple time points, and; 3) an investigation of whether several different measures of intra-individual change demonstrated incremental predictive validity over the most proximal assessment. This approach represented a replication and extension of the framework set out by Lloyd (2015) in a recent thesis for testing whether reassessment of dynamic risk and protective factors enhances the prediction of imminent recidivism. Across all three sections, results provided consistent evidence that the most proximal assessment was the most accurate predictor of imminent recidivism. The most proximal assessment was a significantly more accurate predictor than a baseline assessment, and neither aggregation nor measures of intra-individual change clearly improved predictive accuracy. These results highlight the importance of reassessment for monitoring changes in the likelihood of recidivism over time and have important implications for community correctional agencies who are tasked with managing individuals released from prison, particularly those deemed to be the highest risk of recidivism. The results also have theoretical implications for the concepts of dynamic risk and protective factors and their role in the process leading to recidivism. A better understanding of the recidivism process should lead to intervention strategies that are more effective at reducing recidivism.</p>

Analisis Akurasi Model-Model Prediksi Financial Distress

This study aims to find the most accurate predictor model of financial distress. The company has the potential to go bankrupt. Bankruptcy can be predicted using an accurate predictor model as an early warning to anticipate financial distress. This research was conducted on the global retail industry which is included in Kantar's 2019 Top 30 Global Retails (EUR). The data in this study were taken from 60 annual reports for the 2018-2019 period and a sample of 30 on global retail companies. The accuracy rate is calculated by the number of correct predictions divided by the total data and multiplied by one hundred percent. This study compares four predictor models of financial distress, namely the Altman model, the Springate model, the Taffler model, and the Grover model. With the results of the study, the Grover model has the highest level of accuracy, which is 76.67%.

Do Serial Sonographic Assessments of Fetuses with Isolated Congenital Diaphragmatic Hernia Help Us Predict Survival?

<b><i>Objective:</i></b> The aim of this study was to assess the ability of serial prenatal sonographic measurements, and specifically changes in the observed-to-expected lung-to-head ratio (O/E LHR) throughout gestation and to predict survival in congenital diaphragmatic hernia (CDH). <b><i>Methods:</i></b> Retrospective study of CDH fetuses evaluated prenatally and treated postnatally in a single tertiary center, 2008–2020. Sonographic evaluations included side of herniation, liver involvement, and O/E LHR. All data were calculated to assess ability to predict survival. <b><i>Results:</i></b> Overall, 94 fetuses were evaluated prenatally and delivered in our medical center. Among them, 75 had isolated CDH and 19 nonisolated. CDH was categorized as left (<i>n</i> = 76; 80.8%), right (<i>n</i> = 16; 17.0%), or bilateral (<i>n</i> = 2; 2.2%). Overall perinatal survival rate was 57% for all live-born infants, 68% in isolated CDH, and 40% in nonisolated (excluding 2 cases that underwent fetoscopic endoluminal tracheal occlusion and did not survive). The O/E LHR was lower in cases with perinatal death compared to survivors. In cases with multiple evaluations, the minimal O/E LHR was the most accurate predictor of survival and need for perinatal extracorporeal membrane oxygenation (ECMO) support. This remained significant when excluding twin pregnancies or when evaluating only isolated left CDH. In addition to disease severity, the side of herniation and liver position was associated with preoperative mortality. <b><i>Conclusion:</i></b> O/E LHR is associated with perinatal survival. In cases with multiple evaluations, the minimal O/E LHR is the most accurate and significant predictor of perinatal mortality and need for ECMO support.

TXNDC2 joint molecular marker is associated with testis pathology and is an accurate predictor of sperm retrieval

The association of PRM1/2 with male azoospermia is well-documented, but the relationship between TXNDC2 deficiency and the azoospermia phenotype, sperm retrieval, and pathology has not been elucidated. Here we identified the association of TXNDC2 and protamines in evaluating testis pathology and sperm retrieval. An extensive microarray meta-analysis of men with idiopathic azoospermia was performed, and after undergoing several steps of data quality controls, the data passing QC were pooled and batch effect corrected. As redox imbalance has been shown to have a variable relationship with fertility, our relative expression studies began with candidate protamination and thioredoxin genes. We constructed a logistic regression model of TXNDC2 with PRM1 and PRM2 genes, and collective ROC analysis indicated a sensitivity of 96.8% and specificity of 95.5% with a ROC value of 0.995 (SE = 0.0070, 95% CI 0.982–1.000). These results demonstrate that TXNDC2, PRM1, and PRM2 combined have a robust power to predict sperm retrieval and correlate with severe azoospermia pathology.

Disentangling interoceptive abilities in alexithymia

In recent years, research on interoceptive abilities (i.e., sensibility, accuracy, and awareness) and their associations with emotional experience has flourished. Yet interoceptive abilities in alexithymia—a personality trait characterized by a difficulty in the cognitive interpretation of emotional arousal, which impacts emotional experience—remain under-investigated, thereby limiting a full understanding of subjective emotional experience processing. Research has proposed two contrasting explanations thus far: in one model, the dimensions of interoceptive sensibility and accuracy in alexithymia would increase; in the other model, they would decrease. Surprisingly, the contribution of interoceptive awareness has been minimally researched. In this study (N = 182), the relationship between participants’ level of alexithymia and the three interoceptive dimensions was tested. Our results show that the higher the level of alexithymia is, the higher interoceptive accuracy and sensibility (R2 = 0.29 and R2 = 0.14); conversely, the higher the level of alexithymia is, the lower interoceptive awareness (R2 = 0.36). Moreover, an ROC analysis reveals that interoceptive awareness is the most accurate predictor of alexithymia, yielding over 92% accuracy. Collectively, these results support a coherent understanding of interoceptive abilities in alexithymia, whereby the dissociation of interoceptive accuracy and awareness may explain the underlying psycho-physiological mechanisms of alexithymia. A possible neurocognitive mechanism is discussed which suggests insurgence of psychosomatic disorders in alexithymia and related psychotherapeutic approaches.

Reinforcement Learning Page Prediction for Hierarchically Ordered Municipal Websites

Public websites offer information on a variety of topics and services and are accessed by users with varying skills to browse the kind of electronic document repositories. However, the complex website structure and diversity of web browsing behavior create a challenging task for click prediction. This paper presents the results of a novel reinforcement learning approach to model user browsing patterns in a hierarchically ordered municipal website. We study how accurate predictor the browsing history is, when the target pages are not immediate next pages pointed by hyperlinks, but appear a number of levels down the hierarchy. We compare traditional type of baseline classifiers’ performance against our reinforcement learning-based training algorithm.

An EPIC predictor of gestational age and its application to newborns conceived by assisted reproductive technologies

Background Gestational age is a useful proxy for assessing developmental maturity, but correct estimation of gestational age is difficult using clinical measures. DNA methylation at birth has proven to be an accurate predictor of gestational age. Previous predictors of epigenetic gestational age were based on DNA methylation data from the Illumina HumanMethylation 27 K or 450 K array, which have subsequently been replaced by the Illumina MethylationEPIC 850 K array (EPIC). Our aims here were to build an epigenetic gestational age clock specific for the EPIC array and to evaluate its precision and accuracy using the embryo transfer date of newborns from the largest EPIC-derived dataset to date on assisted reproductive technologies (ART). Methods We built an epigenetic gestational age clock using Lasso regression trained on 755 randomly selected non-ART newborns from the Norwegian Study of Assisted Reproductive Technologies (START)—a substudy of the Norwegian Mother, Father, and Child Cohort Study (MoBa). For the ART-conceived newborns, the START dataset had detailed information on the embryo transfer date and the specific ART procedure used for conception. The predicted gestational age was compared to clinically estimated gestational age in 200 non-ART and 838 ART newborns using MM-type robust regression. The performance of the clock was compared to previously published gestational age clocks in an independent replication sample of 148 newborns from the Prediction and Prevention of Preeclampsia and Intrauterine Growth Restrictions (PREDO) study—a prospective pregnancy cohort of Finnish women. Results Our new epigenetic gestational age clock showed higher precision and accuracy in predicting gestational age than previous gestational age clocks (R2 = 0.724, median absolute deviation (MAD) = 3.14 days). Restricting the analysis to CpGs shared between 450 K and EPIC did not reduce the precision of the clock. Furthermore, validating the clock on ART newborns with known embryo transfer date confirmed that DNA methylation is an accurate predictor of gestational age (R2 = 0.767, MAD = 3.7 days). Conclusions We present the first EPIC-based predictor of gestational age and demonstrate its robustness and precision in ART and non-ART newborns. As more datasets are being generated on the EPIC platform, this clock will be valuable in studies using gestational age to assess neonatal development.