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Evaluation of public programs has undergone many changes over the past four decades since Peter Rossi coined his "Iron Law" of program evaluation: "The expected value of any net impact assessment of any large-scale social program is zero." While that assessment may be somewhat overstated, the essence still holds. The failures far outnumber the successes, and the estimated favorable effects are rarely sizeable. Despite this grim assessment, much can be learned from "failed" experiments, and from ones that are successful in only some sites or subgroups. Advances in study design, statistical models, data, and how inferences are drawn from estimates have substantially improved our analyses and will continue to do so. However, the most actual learning about "what works" (and why, when, and where) is likely to come from gathering more detailed and comprehensive data on how the intervention was implemented and attempting to link that data to estimated impacts. Researchers need detailed data on the target population served, the content of the intervention, and the process by which it is delivered to participating service providers and individuals. Two examples presented here illustrate how researchers drew useful broader lessons from impact estimates for a set of related programs. Rossi posited three reasons most interventions fail—wrong question, wrong intervention, poor implementation. Speeding the accumulation of wisdom about how social programs can best help vulnerable populations will require that researchers work closely with program funders, developers, operators, and participants to gather and interpret these detailed data about program implementation.

This article describes "emotional intelligence" and shows how to integrate emotional intelligence, and emotional skills training, into an undergraduate course. It provides background information on emotions and behavior that is useful in understanding emotional intelligence, how emotions may affect people's behavior, and the relevance of this topic to interpersonal and organizational behavior. Incorporating examples from classroom activities, the article describes three teaching exercises that have proven useful in conveying a sense of the basic precepts of emotional intelligence to students and in helping students develop skills in emotionally intelligent behavior.

Background: Policy makers seek to replace the "thumbs up–thumbs down" of conventional hypothesis testing with statements about the probability that program effects on key outcomes exceed policy-relevant thresholds. Objective: We develop a Bayesian model that addresses the shortcomings of a typical frequentist approach to estimating the effects of the Comprehensive Primary Care (CPC) initiative, a Centers for Medicare and Medicaid Services demonstration. We compare findings from the two approaches to illustrate the relative merits of introducing additional assumptions through Bayesian methods. Research design: We apply Bayesian and frequentist methods to estimate the effects of CPC on total Medicare expenditures per beneficiary per month for Medicare beneficiaries attributed to participating practices. Under both paradigms, we estimated program effects using difference-in-differences regressions comparing the change in Medicare expenditures between baseline and follow-up for Medicare patients attributed to 497 primary care practices participating in CPC to Medicare patients attributed to 908 propensity score-matched comparison practices. Results: Results from the Bayesian and frequentist models are comparable for the overall sample, but in regional subsamples, the Bayesian model produces more precise etimates that exhibit less variation over time. The Bayesian results also permit probabilistic inference about the magnitudes of effects, offering policy makers the ability to draw conclusions about practically meaningful thresholds. Conclusions: Carefully developed Bayesian models can enhance precision and plausibility and offer a more nuanced understanding of where and when program effects occur, without imposing undue assumptions. At the same time, these methods frame conclusions in flexible, intuitive terms that respond directly to policy makers' needs.

BACKGROUND: Physician Asthma Care Education (PACE) is a programme developed in the USA to improve paediatric asthma outcomes. AIMS: To examine translation of PACE to Australia. METHODS: The RE-AIM framework was used to assess translation. Demographic characteristics and findings regarding clinical asthma outcomes from PACE randomised clinical trials in both countries were examined. Qualitative content analysis was used to examine fidelity to intervention components. RESULTS: Both iterations of PACE reached similar target audiences (general practice physicians and paediatric patients with asthma); however, in the USA, more children with persistent disease were enrolled. In both countries, participation comprised approximately 10% of eligible physicians and 25% of patients. In both countries, PACE deployed well-known local physicians and behavioural scientists as facilitators. Sponsorship of the programme was provided by professional associations and government agencies. Fidelity to essential programme elements was observed, but PACE Australia workshops included additional components. Similar outcomes included improvements in clinician confidence in developing short-term and long-term care plans, prescribing inhaled corticosteroids, and providing written management instructions to patients. No additional time was spent in the patient visit compared with controls. US PACE realised reductions in symptoms and healthcare use, results that could not be confirmed in Australia because of limitations in follow-up time and sample sizes. US PACE is maintained through a National Heart, Lung, and Blood Institute website. Development of maintenance strategies for PACE Australia is underway. CONCLUSIONS: Based on criteria of the RE-AIM framework, the US version of PACE has been successfully translated for use in Australia.