Dropout has been identified as a significant problem among military populations seeking psychotherapy (Goetter et al., 2015; Hoge et al., 2014), yet an overall estimate of its exact prevalence and predictors does not exist. The aims of the current meta-analysis were to estimate outpatient psychotherapy dropout rates for this population and evaluate potential moderators of this event. In total, 283 articles—comprising data from 719,465 U.S. service members and veterans—met all inclusion criteria and were included in the meta-analysis. The average weighted dropout rate for all outpatient therapies was 25.6%, 95% CI [22.4%, 29.2%], and prediction interval [1.9%, 85.9%]. Furthermore, dropout was 27.0% for cognitive behavioral therapies (CBTs), 25.3% for trauma treatments, 27.6% for the Department of Veterans Affairs (VA), 28.9% for individual therapies, and 9.8% for intensive outpatient settings. Findings from metaregression analyses using mixed-effects models indicated that higher dropout was linked with the following after accounting for other moderators: younger age, CBTs, nonmanualized approaches, VA versus Department of Defense settings, individual versus group therapies, and weekly versus intensive outpatient formats. Dropout was not linked with other client, therapist, treatment, and research variables. Taken together, dropout estimates were obtained for a range of military populations and treatment characteristics, including theoretical orientation, presenting concern, setting, and therapy formats. These estimates may provide potential benchmarks for therapists, administrators, and policymakers serving military populations. Leveraging dropout prevention strategies with at-risk groups highlighted in this study may enhance mental health care outcomes for this high-need population.

We study changes in political discourse during campaigns, using a novel dataset of candidate websites for US House elections, 2002–2016, and manifestos for French parliamentary and local elections, 1958–2022. We find that candidates move to the center in ideology and rhetorical complexity between the first round (or primary) and the second round (or general election). This convergence reflects candidates' strategic adjustment to their opponents, as predicted by Downsian competition: Using an RDD we show that candidates converge to the platform of opponents who narrowly qualified for the last round as opposed to those who narrowly failed to qualify.