Saving Lives: A Meta-Analysis Of Team Training In Healthcare

Keywords

Meta-analysis; Team training; Teams; Teamwork; Training

Abstract

As the nature of work becomes more complex, teams have become necessary to ensure effective functioning within organizations. The healthcare industry is no exception. As such, the prevalence of training interventions designed to optimize teamwork in this industry has increased substantially over the last 10 years (Weaver, Dy, & Rosen, 2014). Using Kirkpatrick's (1956, 1996) training evaluation framework, we conducted a meta-analytic examination of healthcare team training to quantify its effectiveness and understand the conditions under which it is most successful. Results demonstrate that healthcare team training improves each of Kirkpatrick's criteria (reactions, learning, t s; d = .37 to .89). Second, findings indicate that healthcare team training is largely robust to trainee composition, training strategy, and characteristics of the work environment, with the only exception being the reduced effectiveness of team training programs that involve feedback. As a tertiary goal, we proposed and found empirical support for a sequential model of healthcare team training where team training affects results via learning, which leads to transfer, which increases results. We find support for this sequential model in the healthcare industry (i.e., the current meta-analysis) and in training across all industries (i.e., using meta-analytic estimates from Arthur, Bennett, Edens, & Bell, 2003), suggesting the sequential benefits of training are not unique to medical teams. Ultimately, this meta-analysis supports the expanded use of team training and points toward recommendations for optimizing its effectiveness within healthcare settings.

Publication Date

9-1-2016

Publication Title

Journal of Applied Psychology

Volume

101

Issue

9

Number of Pages

1266-1304

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1037/apl0000120

Socpus ID

84974574062 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/84974574062

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