Concept Mapping Study of Stakeholder Perceptions of Implementation of Cognitive-Behavioral Social Skills Training on Assertive Community Treatment Teams

This study aimed to identify factors associated with implementation of cognitive behavioral social skills training (CBSST) on assertive community treatment (ACT) teams in a large public sector behavioral health system. This study used concept mapping (a mixed-method approach) and involved a sample including diverse stakeholder participants including patients, ACT team members, team leaders, organization leaders, and system leaders. We identified 14 distinct issues related to implementing CBSST on ACT teams: (a) CBSST fit with ACT structure, (b) CBSST fit with ACT process, (c) provider perceptions about CBSST, (d) staff pressures/other demands; (e) CBSST and ACT synergy, (f) client characteristics, (g) benefits of CBSST, (h) coordination/interaction among ACT providers, (i) government/regulatory factors, (j) integration of CBSST into ACT, (k) training support, (l) training resources, (m) multilevel agency leadership, and (n) provider characteristics. Each of these dimensions were rated in regard to importance and changeability with the top 5 rated dimensions including effective training support; alignment of leadership across levels of the community-based organizations delivering services; perceived benefits of CBSST, CBSST and ACT synergy; and provider perceptions of CBSST. The most critical issues for CBSST implementation on ACT teams should be addressed in future studies. Implementation strategies that capitalize on enhancing leadership and organizational climate hold promise to address all of these issues. (PsycInfo Database Record (c) 2021 APA, all rights reserved)

Insights into the accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing the accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. After we provided them with historical trend data on the relevant domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N = 86 teams and 359 forecasts), with an opportunity to update forecasts on the basis of new data six months later (Tournament 2; N = 120 teams and 546 forecasts). Benchmarking forecasting accuracy revealed that social scientists' forecasts were on average no more accurate than those of simple statistical models (historical means, random walks or linear regressions) or the aggregate forecasts of a sample from the general public (N = 802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models and based predictions on prior data. How accurate are social scientists in predicting societal change, and what processes underlie their predictions? Grossmann et al. report the findings of two forecasting tournaments. Social scientists' forecasts were on average no more accurate than those of simple statistical models

Insights into accuracy of social scientists' forecasts of societal change

How well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists’ forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data