A 680,000-person megastudy of nudges to encourage vaccination in pharmacies
Autor: | Katherine L. Milkman, Linnea Gandhi, Mitesh S. Patel, Heather N. Graci, Dena M. Gromet, Hung Ho, Joseph S. Kay, Timothy W. Lee, Jake Rothschild, Jonathan E. Bogard, Ilana Brody, Christopher F. Chabris, Edward Chang, Gretchen B. Chapman, Jennifer E. Dannals, Noah J. Goldstein, Amir Goren, Hal Hershfield, Alex Hirsch, Jillian Hmurovic, Samantha Horn, Dean S. Karlan, Ariella S. Kristal, Cait Lamberton, Michelle N. Meyer, Allison H. Oakes, Maurice E. Schweitzer, Maheen Shermohammed, Joachim Talloen, Caleb Warren, Ashley Whillans, Kuldeep N. Yadav, Julian J. Zlatev, Ron Berman, Chalanda N. Evans, Rahul Ladhania, Jens Ludwig, Nina Mazar, Sendhil Mullainathan, Christopher K. Snider, Jann Spiess, Eli Tsukayama, Lyle Ungar, Christophe Van den Bulte, Kevin G. Volpp, Angela L. Duckworth |
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Rok vydání: | 2022 |
Předmět: | |
Zdroj: | Proceedings of the National Academy of Sciences. 119 |
ISSN: | 1091-6490 0027-8424 |
Popis: | Encouraging vaccination is a pressing policy problem. To assess whether text-based reminders can encourage pharmacy vaccination and what kinds of messages work best, we conducted a megastudy. We randomly assigned 689,693 Walmart pharmacy patients to receive one of 22 different text reminders using a variety of different behavioral science principles to nudge flu vaccination or to a business-as-usual control condition that received no messages. We found that the reminder texts that we tested increased pharmacy vaccination rates by an average of 2.0 percentage points, or 6.8%, over a 3-mo follow-up period. The most-effective messages reminded patients that a flu shot was waiting for them and delivered reminders on multiple days. The top-performing intervention included two texts delivered 3 d apart and communicated to patients that a vaccine was "waiting for you." Neither experts nor lay people anticipated that this would be the best-performing treatment, underscoring the value of simultaneously testing many different nudges in a highly powered megastudy. |
Databáze: | OpenAIRE |
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