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Screening, brief intervention and referral to treatment (SBIRT) in primary care is a burgeoning environmental treatment strategy for illicit and prescription drug abuse and a variety of other health behaviors. While clinical research on SBIRT's efficacy continues to produce positive results, translational research focusing on the integration of the evidence-based processes into primary care settings has been less prevalent. This paper describes the decisions made in the design of the Indiana SBIRT project and describes several barriers that prevented eligible patients from receiving services provided through SBIRT. It then elaborates on the qualitative mechanisms used to identify solutions to those barriers and provides preliminary quantitative evidence for the effectiveness of the solutions that were implemented. The intention of this translational research is to provide a broad perspective on program improvement so that other SBIRT projects in the United States and internationally might benefit from the lessons learned by Indiana SBIRT.

Prospective longitudinal data collection is an important way for researchers and evaluators to assess change. In school-based settings, for low-risk and/or likely-beneficial interventions or surveys, data quality and ethical standards are both arguably stronger when using a waiver of parental consent—but doing so often requires the use of anonymous data collection methods. The standard solution to this problem has been the use of a self-generated identification code. However, such codes often incorporate personalized elements (e.g., birth month, middle initial) that, even when meeting the technical standard for anonymity, may raise concerns among both youth participants and their parents, potentially altering willingness to participate, response quality, or generating outrage. There may be value, therefore, in developing a self-generated identification code and matching approach that not only is technically anonymous but also appears anonymous to a research-naive individual. This article provides a proof of concept for a novel matching approach for school-based longitudinal data collection that potentially accomplishes this goal.

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