Bronsoler, A., Doyle, J. J. & Van Reenen, J. M. The impact of healthcare it on clinical quality, productivity and workers. SSRN Electron. J. 14, 23–46 (2021).
Rajpurkar, P., Chen, E., Banerjee, O. & Topol, E. J. AI in health and medicine. Nat. Med. 28, 31–38 (2022).
Goh, K. H. et al. Artificial intelligence in sepsis early prediction and diagnosis using unstructured data in healthcare. Nat. Commun. 12, 711 (2021).
Tomašev, N. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019).
Young, A. T., Amara, D., Bhattacharya, A. & Wei, M. L. Patient and general public attitudes towards clinical artificial intelligence: a mixed methods systematic review. Lancet Digit. Health 3, e599–e611 (2021).
Logg, J. M., Minson, J. A. & Moore, D. A. Algorithm appreciation: people prefer algorithmic to human judgment. Organ. Behav. Hum. Decis. Process. 151, 90–103 (2019).
Dietvorst, B. J., Simmons, J. P. & Massey, C. Algorithm aversion: people erroneously avoid algorithms after seeing them err. J. Exp. Psychol. Gen. 144, 114–126 (2015).
Longoni, C., Bonezzi, A. & Morewedge, C. K. Resistance to medical artificial intelligence. J. Consum. Res. 46, 629–650 (2019).
Fridman, A., Gershon, R. & Gneezy, A. COVID-19 and vaccine hesitancy: a longitudinal study. PLoS ONE 16, e0250123 (2021).
MacIntyre, C. R., Mahimbo, A., Moa, A. M. & Barnes, M. Influenza vaccine as a coronary intervention for prevention of myocardial infarction. Heart 102, 1953–1956 (2016).
Warren-Gash, C., Smeeth, L. & Hayward, A. C. Influenza as a trigger for acute myocardial infarction or death from cardiovascular disease: a systematic review. Lancet Infect. Dis. 9, 601–610 (2009).
People at increased risk for flu complications. Centers for Disease Control and Prevention https://www.cdc.gov/flu/highrisk/index.htm (2022).
Goeijenbier, M. et al. Benefits of flu vaccination for persons with diabetes mellitus: a review. Vaccine 35, 5095–5101 (2017).
Nichol, K. L. Efficacy and effectiveness of influenza vaccination. Vaccine 26, D17–D22 (2008).
Troeger, C. E. et al. Mortality, morbidity, and hospitalisations due to influenza lower respiratory tract infections, 2017: an analysis for the Global Burden of Disease Study 2017. Lancet Respir. Med. 7, 69–89 (2019).
Bonezzi, A., Ostinelli, M. & Melzner, J. The human black-box: the illusion of understanding human better than algorithmic decision-making. J. Exp. Psychol. Gen. 151, 2250–2258 (2022).
Cadario, R., Longoni, C. & Morewedge, C. K. Understanding, explaining, and utilizing medical artificial intelligence. Nat. Hum. Behav. 5, 1636–1642 (2021).
Pezzo, M. V. & Beckstead, J. W. Patients prefer artificial intelligence to a human provider, provided the AI is better than the human: a commentary on Longoni, Bonezzi and Morewedge (2019). Judgm. Decis. Mak. 15, 443–445 (2020).
Adadi, A. & Berrada, M. Peeking inside the black-box: a survey on explainable artificial intelligence (XAI). IEEE Access 6, 52138–52160 (2018).
Miller, T. Explanation in artificial intelligence: insights from the social sciences. Artif. Intell. 267, 1–38 (2019).
Mercado, J. E. et al. Intelligent agent transparency in human-agent teaming for multi-UxV management. Hum. Factors 58, 401–415 (2016).
Stubbs, K., Wettergreen, D. & Hinds, P. J. Autonomy and common ground in human–robot interaction: a field study. IEEE Intell. Syst. 22, 42–50 (2007).
Benartzi, S. et al. Should governments invest more in nudging?. Psychol. Sci. 28, 1041–1055 (2017).
Patel, M. S., Volpp, K. G. & Asch, D. A. Nudge units to improve the delivery of health care. New Engl. J. Med. 378, 214–216 (2018).
Dai, H. et al. Behavioural nudges increase COVID-19 vaccinations. Nature 597, 404–409 (2021).
Milkman, K. L. et al. A megastudy of text-based nudges encouraging patients to get vaccinated at an upcoming doctor’s appointment. Proc. Natl Acad. Sci. USA 118, 10–12 (2021).
Milkman, K. L. et al. A 680,000-person megastudy of nudges to encourage vaccination in pharmacies. Proc. Natl Acad. Sci. USA 119, e2115126119 (2022).
Santos, H. C., Goren, A., Chabris, C. F. & Meyer, M. N. Effect of targeted behavioral science messages on COVID-19 vaccination registration among employees of a large health system: a randomized trial. JAMA Netw. Open 4, 5–8 (2021).
Schneider, F. H. et al. Financial incentives for vaccination do not have negative unintended consequences. Nature 613, 526–533 (2023).
Yokum, D., Lauffenburger, J. C., Ghazinouri, R. & Choudhry, N. K. Letters designed with behavioural science increase influenza vaccination in Medicare beneficiaries. Nat. Hum. Behav. 2, 743–749 (2018).
Chen, N., Trump, K.-S., Hall, S. & Le, Q. The effect of postcard reminders on vaccinations among the elderly: a block-randomized experiment. Behav. Public Policy 7, 240–265 (2020).
Rabb, N. et al. Evidence from a statewide vaccination RCT shows the limits of nudges. Nature 604, E1–E7 (2022).
Szilagyi, P. G. et al. Effect of personalized messages sent by a health system’s patient portal on influenza vaccination rates: a randomized clinical trial. J. Gen. Intern. Med. 37, 615–623 (2022).
Modin, D. et al. Effect of electronic nudges on influenza vaccination rate in older adults with cardiovascular disease: prespecified analysis of the NUDGE-FLU trial. Circulation 147, 1345–1354 (2023).
Johansen, N. D. et al. Electronic nudges to increase influenza vaccination uptake among patients with heart failure: a prespecified analysis of the NUDGE-FLU trial. Eur. J. Heart Fail. 25, 1450–1458 (2023).
Brewer, N. T., Chapman, G. B., Rothman, A. J., Leask, J. & Kempe, A. Increasing vaccination: putting psychological science into action. Psychol. Sci. Public Interest 18, 149–207 (2017).
Sheeran, P., Harris, P. R. & Epton, T. Does heightening risk appraisals change people’s intentions and behavior? A meta-analysis of experimental studies. Psychol. Bull. 140, 511–543 (2014).
Motta, M., Sylvester, S., Callaghan, T. & Lunz-Trujillo, K. Encouraging COVID-19 vaccine uptake through effective health communication. Front. Polit. Sci. 3, 630133 (2021).
Reñosa, M. D. C. et al. Nudging toward vaccination: a systematic review. BMJ Glob. Health 6, e006237 (2021).
French, D. P., Cameron, E., Benton, J. S., Deaton, C. & Harvie, M. Can communicating personalised disease risk promote healthy behaviour change? A systematic review of systematic reviews. Ann. Behav. Med. 51, 718–729 (2017).
Schulberg, S. D. et al. Cardiovascular risk communication strategies in primary prevention. A systematic review with narrative synthesis. J. Adv. Nurs. 78, 3116–3140 (2022).
Brewer, N. T. What works to increase vaccination uptake. Acad. Pediatr. 21, S9–S16 (2021).
Tuckerman, J. et al. Short message service reminder nudge for parents and influenza vaccination uptake in children and adolescents with special risk medical conditions. JAMA Pediatr. 177, 337–344 (2023).
Johansen, N. D. et al. Electronic nudges to increase influenza vaccination uptake in Denmark: a nationwide, pragmatic, registry-based, randomised implementation trial. Lancet 401, 1103–1114 (2023).
Sääksvuori, L. et al. Information nudges for influenza vaccination: evidence from a large-scale cluster-randomized controlled trial in Finland. PLoS Med. 19, e1003919 (2022).
Wolk, D. M. et al. Prediction of influenza complications: development and validation of a machine learning prediction model to improve and expand the identification of vaccine-hesitant patients at risk of severe influenza complications. J. Clin. Med. 11, 4342 (2022).
La Macchia, S. T., Louis, W. R., Hornsey, M. J. & Leonardelli, G. J. In small we trust: lay theories about small and large groups. Pers. Soc. Psychol. Bull. 42, 1321–1334 (2016).
Rudert, S. C., Reutner, L., Greifeneder, R. & Walker, M. Faced with exclusion: perceived facial warmth and competence influence moral judgments of social exclusion. J. Exp. Soc. Psychol. 68, 101–112 (2017).
Clark, J. K., Thiemb, K. C., Hoover, A. E. & Habashi, M. M. Gender stereotypes and intellectual performance: stigma consciousness as a buffer against stereotype validation. J. Exp. Soc. Psychol. 68, 185–191 (2017).
Mann, T. C. & Ferguson, M. J. Reversing implicit first impressions through reinterpretation after a two-day delay. J. Exp. Soc. Psychol. 68, 122–127 (2017).
Filiz, I., Judek, J. R., Lorenz, M. & Spiwoks, M. The extent of algorithm aversion in decision-making situations with varying gravity. PLoS ONE 18, e0278751 (2023).
Waters, E. A. et al. Translating cancer risk prediction models into personalized cancer risk assessment tools: stumbling blocks and strategies for success. Cancer Epidemiol. Biomarkers Prev. 29, 2389–2394 (2020).
Hasselblad, V. & Hedges, L. V. Meta-analysis of screening and diagnostic tests. Psychol. Bull. 117, 167–178 (1995).
Castelo, N., Bos, M. W. & Lehmann, D. R. Task-dependent algorithm aversion. J. Mark. Res. 56, 809–825 (2019).
Saccardo, S. et al. Field testing the transferability of behavioural science knowledge on promoting vaccinations. Nat. Hum. Behav. 8, 878–890 (2024).
DellaVigna, S. & Linos, E. RCTs to scale: comprehensive evidence from two nudge units. Econometrica 90, 81–116 (2022).
Service, O. et al. EAST: four simple ways to apply behavioural insights. Annu. Rev. of Policy Des. 5, 1–53 (2017).
Jacobson, M., Chang, T. Y., Shah, M., Pramanik, R. & Shah, S. B. Can financial incentives and other nudges increase COVID-19 vaccinations among the vaccine hesitant? A randomized trial. Vaccine 40, 6235–6242 (2022).
Campos-Mercade, P. et al. Monetary incentives increase COVID-19 vaccinations. Science 374, 879–882 (2021).
Bryan, C. J., Tipton, E. & Yeager, D. S. Behavioural science is unlikely to change the world without a heterogeneity revolution. Nat. Hum. Behav. 5, 980–989 (2021).
Brody, I. et al. Targeting behavioral interventions based on past behavior: evidence from vaccine uptake. Organ. Behav. Hum. Decis. Process. 192, 104465 (2026).
de Ridder, D., Kroese, F. & van Gestel, L. Nudgeability: mapping conditions of susceptibility to nudge influence. Perspect. Psychol. Sci. 17, 346–359 (2022).
Chater, N. & Loewenstein, G. The i-frame and the s-frame: how focusing on individual-level solutions has led behavioral public policy astray. Behav. Brain Sci. 46, e147 (2022).
Funk, C., Hefferon, M., Kennedy, B. & Johnson, C. Trust and Mistrust in Americans’ Views of Scientific Experts. https://www.pewresearch.org/science/2019/08/02/trust-and-mistrust-in-americans-views-of-scientific-experts/ (Pew Research Center, 2019).
Pasquini, G., Stocking, G., Kikuchi, E., Pula, I. & Kirzinger, A. Where Do Americans Get Health Information, and What Do They Trust? https://www.pewresearch.org/science/2026/04/07/where-do-americans-get-health-information-and-what-do-they-trust/ (Pew Research Center, 2026).
Saccardo, S. et al. Scaling Nudges: Who Moves and How. https://doi.org/10.2139/ssrn.3971192 (SSRN, 2025).
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing https://www.r-project.org (2021).
Coppock, A. randomizr: easy-to-use tools for common forms of random assignment and sampling. Comprehensive R Archive Network (CRAN) https://cran.r-project.org/web/packages/randomizr/index.html (2024).
Colling, L. J. bayesplay: the bayes factor playground. R package version 0.9.3. GitHub https://github.com/bayesplay/bayesplay (2023).
Rich, B. table1: tables of descriptive statistics in HTML. CRAN: Contributed Packages https://doi.org/10.32614/CRAN.package.table1 (2025).
Wickham, H. stringr: simple, consistent wrappers for common string operations. Comprehensive R Archive Network (CRAN) https://cran.r-project.org/web/packages/stringr/index.html (2025).
Lüdecke, D. sjPlot: data visualization for statistics in social science. Comprehensive R Archive Network (CRAN) https://CRAN.R-project.org/package=sjPlot (2025).
Coppock, A. randomizr: easy-to-use tools for common forms of random assignment and sampling. Comprehensive R Archive Network (CRAN) https://cran.r-project.org/web/packages/randomizr/index.html (2025).
Champely, S. pwr: basic functions for power analysis. GitHub https://github.com/heliosdrm/pwr (2023).
Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).
Sievert, C. Interactive Web-Based Data Visualization with R, Plotly, and Shiny (CRC, 2020).
Rizopoulos, D. ltm: an R package for latent variable modeling and item response theory analyses. J. Stat. Softw. 17, 1–25 (2006).
Grolemund, G. & Wickham, H. Dates and times made easy with {lubridate}. J. Stat. Softw. 40, 1–25 (2011).
Long, J. A. jtools: analysis and presentation of social scientific data. J. Open Source Softw. 9, 6610 (2024).
Hugh-Jones, D. ggmagnify: create a magnified inset of part of a ‘Ggplot’ object. GitHub https://github.com/hughjonesd/ggmagnify (2026).
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).
Lenth, R. V. & Piaskowski, J. emmeans: estimated marginal means, aka least-squares means. Comprehensive R Archive Network (CRAN) https://cran.r-project.org/web/packages/emmeans/index.html (2026).
Gomila, R. Logistic or linear? Estimating causal effects of experimental treatments on binary outcomes using regression analysis. J. Exp. Psychol. Gen. 150, 700–709 (2021).
Hellevik, O. Linear versus logistic regression when the dependent variable is a dichotomy. Qual. Quant. 43, 59–74 (2009).
Wetzels, R. et al. Statistical evidence in experimental psychology: an empirical comparison using 855 t tests. Perspect. Psychol. Sci. 6, 291–298 (2011).
Dienes, Z. Using Bayes to get the most out of non-significant results. Front. Psychol. 5, 781 (2014).