Effects of Training on Social Work, Nursing and Medical Trainees' Knowledge, Attitudes and Beliefs Related to Screening and Brief Intervention for Alcohol Use

Indiana University's Schools of Social Work, Nursing and Medicine formed a consortium to advance education for Screening Brief Intervention and Referral to Treatment (SBIRT). Trainees participated in SBIRT training and completed data collection before, immediately after, and 30 days after a face-to-face training. The study explored participants' perceptions about the training and the likelihood of implementing SBI in practice, including attitudes and beliefs that may be predictive of SBIRT utilization in clinical practice. Results show the training targeting SBI and MI behaviors may improve participants' self-reported competence with SBI. This improvement was consistent and strong in all programs. The study results also provided a preliminary indication that the training affected participants' perception of time utilization and compensation for performing SBI

Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions

YesAlthough many modelling and prediction frameworks for corporate bankruptcy and distress have been proposed, the relative performance evaluation of prediction models is criticised due to the assessment exercise using a single measure of one criterion at a time, which leads to reporting conflicting results. Mousavi et al. (Int Rev Financ Anal 42:64–75, 2015) proposed an orientation-free super-efficiency DEA-based framework to overcome this methodological issue. However, within a super-efficiency DEA framework, the reference benchmark changes from one prediction model evaluation to another, which in some contexts might be viewed as “unfair” benchmarking. In this paper, we overcome this issue by proposing a slacks-based context-dependent DEA (SBM-CDEA) framework to evaluate competing distress prediction models. In addition, we propose a hybrid crossbenchmarking- cross-efficiency framework as an alternative methodology for ranking DMUs that are heterogeneous. Furthermore, using data on UK firms listed on London Stock Exchange, we perform a comprehensive comparative analysis of the most popular corporate distress prediction models; namely, statistical models, under both mono criterion and multiple criteria frameworks considering several performance measures. Also, we propose new statistical models using macroeconomic indicators as drivers of distress

Run length and the predictability of stock price reversals

Survival analysis is used to estimate time-varying probabilities of price reversals using daily data for the Australian All Ordinaries Price Index. Lagged price changes lead to persistence (shortening) in a price run if they are of the same (opposite) sign as the run. An increase in the number of runs observed in the previous 30 days also increases the probability of price reversal. The predictive accuracy of the models is assessed using a probability scoring rule. Consistent with market efficiency, the estimated models are less accurate than the random walk model in predicting the length of individual price runs out-of-sample. Copyright 2005 Accounting and Finance Association of Australia and New Zealand..