Target populations have always been a thorny issue for correctional programs, primarily in response to the question "what works for whom?" In this experiment of seamless treatment for probationers in two sites, offenders were randomly assigned to the seamless model (drug treatment incorporated into probation supervision) or traditional referral model to services in the community. The experiment blocked on risk level, using a version of the Wisconsin Risk Tool, to measure the differential effects on rearrest and substance abuse. The seamless system model improved treatment participation with greater gains for the high-risk offenders in both sites. Yet, no main effects were observed on drug use or rearrest, although effect sizes illustrate that small effects can be observed for the high-risk offenders and the direction of the effect size demonstrates negative effects for moderate-risk offenders in one of the sites. Part of the failure to observe main effects may be due to instrumentation and measurement problems, namely that many of the substance abusers in the experiment had low severity substance abuse problems and the majority of the offenders were marijuana users which has a weaker crime-drug linkage. Study findings illustrate the importance of theoretically driven and dynamic risk and need measures. The focus on sound dynamic factors may assist with identifying the appropriate target populations for correctional interventions.Keywords substance abusers; risk assessment; probation; marijuana users; responsivity A cornerstone of the "what works in corrections" literature is that high risk offenders are better suited for more intensive, structured interventions. Andrews and Bonta (1998) premiered the concept in the Psychology of Criminal Conduct, although researchers have been developing the concept for nearly 50 years. Researchers and program evaluators have since suggested that correctional agencies would be better suited to identify high risk offenders and place those offenders in appropriate services, and that the services themselves need to be multi-dimensional to affect the likelihood of desistence. This is essentially the risk-need-responsivity (RNR) concept where the risk and needs of the offender should drive the selection of an appropriate program that can address the criminogenic factors. The RNR grew out of the treatment classification literature that was developed in the 1960s and 1970s by Lee Sechrest, Ted Palmer, and others. RNR also follows a long tradition in the service research literature where matching was introduced to suggest that offenders (or addicts or those with mental health disorders, depending on the literature) should be linked to appropriate services based on the individual's psychological and social needs. NIH-PA Author ManuscriptNIH-PA Author Manuscript NIH-PA Author ManuscriptUsing a services research approach to examine the efficacy of the RNR model requires consideration of how the different components of the model are achieved given individual characteristics of offe...
Short-term indoor e-cigarette use produced accumulation of nicotine on surfaces and clothing, which could lead to dermal exposure to nicotine. Short-term e-cigarette use produced elevated PM2.5 and ultrafine particles, which could lead to secondhand inhalation of these particles and any chemicals associated with them by bystanders. We measured significant differences in PM2.5 and ultrafine particles between disposable e-cigarettes and tank-style e-cigarettes, suggesting a difference in the exposure profiles of e-cigarette products.
This study reveals some of the challenges to experimental cigar research and illustrates the need to characterize cigar products (eg, nicotine and tobacco content) before use in clinical studies. Additional studies and characterization of the physical and chemical properties of cigars may be useful to further understand these products' toxicity, abuse potential, and public health impact.
Objectives: Published employee absenteeism estimates during an influenza pandemic range from 10 to 40 percent. The purpose of this study was to estimate daily employee absenteeism through the duration of an influenza pandemic and to determine the relative impact of key variables used to derive the estimates.Design: Using the Centers for Disease Control and Prevention’s FluWorkLoss program, the authors estimated the number of absent employees on any given day over the course of a simulated 8-week pandemic wave by using varying attack rates. Employee data from a university with a large academic health system were used. Sensitivity of the program outputs to variation in predictor (inputs) values was assessed. Finally, the authors examined and documented the algorithmic sequence of the program.Results: Using a 35 percent attack rate, a total of 47,270 workdays (or 3.4 percent of all available workdays) would be lost over the course of an 8-week pandemic among a population of 35,026 employees. The highest (peak) daily absenteeism estimate was 5.8 percent (minimum 4.8 percent; maximum 7.4 percent). Sensitivity analysis revealed that varying days missed for nonhospitalized illness had the greatest potential effect on peak absence rate (3.1 to 17.2 percent). Peak absence with 15 and 25 percent attack rates were 2.5 percent and 4.2 percent, respectively.Conclusions: The impact of an influenza pandemic on employee availability may be less than originally thought, even with a high attack rate. These data are generalizable and are not specific to institutions of higher education or medical centers. Thus, these findings provide realistic and useful estimates for influenza pandemic planning for most organizations.
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