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Background The relationship between healthcare service accessibility in the community and incarceration is an important, yet not widely understood, phenomenon. Community behavioral health and the criminal legal systems are treated separately, which creates a competing demand to confront mass incarceration and expand available services. As a result, the relationship between behavioral health services, demographics and community factors, and incarceration rate has not been well addressed. Understanding potential drivers of incarceration, including access to community-based services, is necessary to reduce entry into the legal system and decrease recidivism. This study identifies county-level demographic, socioeconomic, healthcare services availability/accessibility, and criminal legal characteristics that predict per capita jail population across the U.S. More than 10 million individuals pass through U.S. jails each year, increasing the urgency of addressing this challenge. Methods The selection of variables for our model proceeded in stages. The study commenced by identifying potential descriptors and then using machine learning techniques to select non-collinear variables to predict county jail population per capita. Beta regression was then applied to nationally available data from all 3,141 U.S. counties to identify factors predicting county jail population size. Data sources include the Vera Institute’s incarceration database, Robert Wood Johnson Foundation’s County Health Rankings and Roadmaps, Uniform Crime Report, and the U.S. Census. Results Fewer per capita psychiatrists (z-score = -2.16; p = .031), lower percent of drug treatment paid by Medicaid (-3.66; p < .001), higher per capita healthcare costs (5.71; p < .001), higher number of physically unhealthy days in a month (8.6; p < .001), lower high school graduation rate (-4.05; p < .001), smaller county size (-2.66, p = .008; -2.71, p = .007; medium and large versus small counties, respectively), and more police officers per capita (8.74; p < .001) were associated with higher per capita jail population. Controlling for other factors, violent crime rate did not predict incarceration rate. Conclusions Counties with smaller populations, larger percentages of individuals that did not graduate high school, that have more health-related issues, and provide fewer community treatment services are more likely to have higher jail population per capita. Increasing access to services, including mental health providers, and improving the affordability of drug treatment and healthcare may help reduce incarceration rates.

The rhetoric and philosophy of community supervision is moving toward rehabilitation, spurred in part by the emergence of evidence-based practices. However, there remains a great deal of tension between punitive and rehabilitative approaches in supervision. This chapter offers a brief history of this tension, with consideration of how macro-level sociopolitical forces affect micro-level practitioner efforts to balance their dual roles as helper and enforcer. Following this, we focus on the ways the tension appears in the present-day context by providing an empirical review of both the punitive sanctions and rehabilitative techniques routinely used in supervision. Our review of rehabilitative techniques includes the dominant risk-need-responsivity (RNR) framework and some of its embedded tools: cognitive-behavioral therapy (CBT), working alliance (WA), and motivational interviewing (MI). We discuss emergent strength-based approaches (SBAs) which shift the focus from an individual's potential risks to their extant strengths. Based on the evidence supporting the RNR, an integrated RNR and SBAs approach will provide a holistic approach to supervision and offer opportunities to provide effective treatment services.

In pursuit of a more robust provenance in the field of species distribution modelling, an extensive literature search was undertaken to find the typical default values, and the range of values, for configuration settings of a number of the most commonly used statistical algorithms available for constructing species distribution models (SDM), as implemented in the R script packages (such as Dismo and Biomod2) or other species distribution modelling programs like Maxent. We found that documentation of SDM algorithm configuration option settings in the SDM literature is very uncommon, and the justifications for these settings were minimal, when present. Such settings were often the R default values, or were the result of trial and error. This is potentially concerning for a number of reasons; it detracts from the robustness of the provenance for such SDM studies; a lack of documentation of configuration option settings in a paper prevents the replication of an experiment, which contravenes one of the main tenets of the scientific method. Inappropriate or uninformed configuration option settings are particularly concerning if they represent a poorly understood ecological variable or process, and if the algorithm is sensitive to such settings; this could result in erroneous and/or unrealistic SDMs. We test the sensitivity of two commonly used SDM algorithms to variation in configuration options settings: Random Forests and Boosted Regression Trees. A process of expert elicitation was used to derive a range of appropriate values with which to test the sensitivity of our algorithms. We chose to use species occurrence records for the Koala (Phascolartos cinereus) for our sensitivity tests, since the species has a well known distribution. Results were assessed by comparing the geospatial distribution from each sensitivity test (i.e. altered-settings) SDM for differences compared to the control SDM (i.e. default settings), using geographical information systems (QGIS). In addition, two performance measures were used to compare differences among the altered-setting SDMs to the control. The aim of our study was to be able to draw conclusions as to how reliable reported SDM results may be in light of the sensitivity of their algorithms to certain settings, given the often arbitrary nature of such settings, and the lack of awareness of, and/or attendance to this issue in most of the published SDM literature. Our results indicate that all two algorithms tested showed sensitivity to alternate values for some of their settings. Therefore this study has showed that the choice of configuration option settings in Random Forests and Boosted Regression Trees has an impact on the results, and that assigning suitable values for these settings is a relevant consideration and as such should be always published along with the model.

Parole in the United States serves as both a mechanism of early release from incarceration, as well as the period of supervision that may follow release, early or otherwise. Attached to the concept of parole, writ large, are multiple, seemingly irreconcilable perspectives regarding its purpose. Yet, evidence exists to suggest that all these perspectives are simultaneously reflected in the microlevel discretionary actions of parole practitioners and the macrolevel policies of the parole system. This is suggestive of a complex interplay between the individual discretion exercised by parole practitioners and the formalized legal reforms that, in some cases, attempt to limit such discretion. This article examines three stages of parole—release, supervision, and revocation—to explicate how practitioners use their discretion to resist or subvert reforms designed to curtail that discretion. Ultimately, these forms of resistance have both practical and theoretical implications for the future of parole and policies aimed at its reform.