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Exploring the Correspondence Between General Correctional Programming and Inmate Misconduct Using a Time-Course Framework.

ABSTRACT: Inmate misconduct continues to threaten safety and order within correctional institutions. Yet few studies have examined its longitudinal nature. In this paper we explore the correspondence between correctional programming and inmate misconduct. To do this, we draw from Linning et al.'s time-course framework devised to improve the design and evaluation of interventions by considering effects that can occur before, during, and after programming. We provide the first empirical demonstration of their framework using prisoner misconduct data collected from all Ohio prisons between January 2008 and June 2012. A cross-lagged panel analysis provides support for the use of a time-course framework. Results show that misconduct decreased during programming. However, we observed increases in misconduct prior to and following exposure to programming. Our results suggest that future work needs to improve our understanding of causal mechanisms of inmate misconduct and when their effects are expected.

SUBMITTER: Linning SJ

PROVIDER: S-EPMC8721553 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Mental and physical health in prison: how co-occurring conditions influence inmate misconduct.

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Reduced Programming Time and Strong Symptom Control Even in Chronic Course Through Imaging-Based DBS Programming.

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Time course analysis of large-scale gene expression in incised muscle using correspondence analysis.

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Time course analysis of large-scale gene expression in incised muscle using correspondence analysis

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2019-11-19 | GSE140517 | GEO

Substance Use Disorder and Correctional Health: Social Justice Course Development.

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A recursive framework for predicting the time-course of drug sensitivity.

Project description:The biological processes involved in a drug's mechanisms of action are oftentimes dynamic, complex and difficult to discern. Time-course gene expression data is a rich source of information that can be used to unravel these complex processes, identify biomarkers of drug sensitivity and predict the response to a drug. However, the majority of previous work has not fully utilized this temporal dimension. In these studies, the gene expression data is either considered at one time-point (before the administration of the drug) or two time-points (before and after the administration of the drug). This is clearly inadequate in modeling dynamic gene-drug interactions, especially for applications such as long-term drug therapy. In this work, we present a novel REcursive Prediction (REP) framework for drug response prediction by taking advantage of time-course gene expression data. Our goal is to predict drug response values at every stage of a long-term treatment, given the expression levels of genes collected in the previous time-points. To this end, REP employs a built-in recursive structure that exploits the intrinsic time-course nature of the data and integrates past values of drug responses for subsequent predictions. It also incorporates tensor completion that can not only alleviate the impact of noise and missing data, but also predict unseen gene expression levels (GEXs). These advantages enable REP to estimate drug response at any stage of a given treatment from some GEXs measured in the beginning of the treatment. Extensive experiments on two datasets corresponding to multiple sclerosis patients treated with interferon are included to showcase the effectiveness of REP.

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A statistical framework for biomarker discovery in metabolomic time course data.

Project description:MotivationMetabolomics is the study of the complement of small molecule metabolites in cells, biofluids and tissues. Many metabolomic experiments are designed to compare changes observed over time under two experimental conditions or groups (e.g. a control and drug-treated group) with the goal of identifying discriminatory metabolites or biomarkers that characterize each condition. A common study design consists of repeated measurements taken on each experimental unit thus producing time courses of all metabolites. We describe a statistical framework for estimating time-varying metabolic profiles and their within-group variability and for detecting between-group differences. Specifically, we propose (i) a smoothing splines mixed effects (SME) model that treats each longitudinal measurement as a smooth function of time and (ii) an associated functional test statistic. Statistical significance is assessed by a non-parametric bootstrap procedure.ResultsThe methodology has been extensively evaluated using simulated data and has been applied to real nuclear magnetic resonance spectroscopy data collected in a preclinical toxicology study as part of a larger project lead by the COMET (Consortium for Metabonomic Toxicology). Our findings are compatible with the previously published studies.AvailabilityAn R script is freely available for download at http://www2.imperial.ac.uk/~gmontana/sme.htm.

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Alcohol Production, Prevention Strategies, and Inmate Knowledge About the Risk for Botulism From Pruno Consumption in a Correctional Facility--Arizona, 2013.

Project description:During July to November 2012, two botulism outbreaks (12 cases total) occurred in one all-male prison; both were associated with illicitly brewed alcohol (pruno) consumption. Inmate surveys were conducted to evaluate and develop prevention and education strategies. Qualitative surveys with open-ended questions were performed among inmates from rooms where outbreaks occurred to learn about pruno consumption. Quantitative surveys assessed knowledge gained after the outbreaks and preferred information sources. For the quantitative surveys, 250 inmates were randomly selected by bed from across the correctional facility and 164 inmates were interviewed. Only 24% of inmates reported any botulism knowledge before the outbreaks and education outreach, whereas 73% reported knowledge after the outbreaks (p < .01). Preferred information sources included handouts/fliers (52%) and the prison television channel (32%).

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