Risk literacy assessment of medical students in Brazil

Authors

DOI:

https://doi.org/10.5712/rbmfc18(45)3802

Keywords:

Education, medical, Predictive value of tests, Mass screening, Risk, Measures of association, exposure, risk or outcome.

Abstract

Background: In order to accomplish the shared decision-making process, it is essential that health professionals are able to understand statistical data of the best available evidence, so that this information can be communicated to their patients. In this context, risk literacy is the ability to evaluate risks and benefits of a given action. Despite the relevance of this skill, research has demonstrated that many professionals and students have difficulty interpreting concepts of statistics and probability, therefore having low risk literacy. Objectives: This study aimed to evaluate risk literacy in medical students and how it impacts their ability to solve a problem concerning the positive predictive value (PPV) of a mass screening. Methods: Medical students from the 4th, 5th, and 6th years of the School of Medicine of Universidade de São Paulo were invited to answer a questionnaire comprised of the Berlin Numeracy Test (BNT), a validated instrument to measure numeracy, and a clinical problem regarding the PPV of a mammogram in the context of mass screening. To measure the level of risk literacy of medical students and to investigate whether there is an association between score in the BNT and the ability to correctly answer the clinical problem regarding PPV. Results: A total of 97 responses were collected, of which 19 (19.52%) participants answered 3 out of 4 questions of the BNT, and 61 (62.89%) correctly answered all the questions. In the clinical problem about PPV of cancer screening, there were 43 correct answers (44.33%). The mean BNT score of the participants was 3.41. Among the students who correctly answered the problem, the mean score was 3.76, and among the ones who answered incorrectly, it was 3.21. Conclusions: Despite the high numeracy measured by the BNT, students had a poor outcome in the clinical problem. This study reinforces previous findings that risk literacy is a difficult skill to be learned, even in individuals with high numeracy. However, the low response rate hinders a more precise interpretation of the results.

Downloads

Download data is not yet available.

Metrics

Metrics Loading ...

Author Biographies

Fabio Yuji Furukawa, Faculdade de Medicina da Universidade de São Paulo – São Paulo (SP), Brasil.

Preceptor at the Primary Care clerkship of Univeristy of São Paulo

Itamar Santos, Faculdade de Medicina da Universidade de São Paulo – São Paulo (SP), Brasil.

Professor at University of São Paulo

Gustavo Diniz Ferreira Gusso, Faculdade de Medicina da Universidade de São Paulo – São Paulo (SP), Brasil.

Professor at Univeristy of São Paulo

References

Stewart M, Brown JB, Weston WW, McWhinney IR, McWilliam CL, Freeman TR. O terceiro componente: elaborando um plano conjunto de manejo dos problemas. In: Medicina centrada na pessoa: transformando o método clínico. 3. ed. ARTMED; 2017. p. 146-86.

Montori VM, Elwyn G, Devereaux P, Strauss SE, Haynes RB, Guyatt G. Decision Making and the Patient. In: Users’ guides to the medical literature. 3. ed. New York: McGraw-Hill Medical; 2015. p. 389-415.

Wegwarth O, Gigerenzer G. The barrier to informed choice in cancer screening: statistical illiteracy in physicians and patients. Recent Results Cancer Res 2018:210:207-221. https://doi.org/10.1007/978-3-319-64310-6_13 DOI: https://doi.org/10.1007/978-3-319-64310-6_13

Soto-Mota A, Carrillo Maravilla E, Fragoso JM, Castejón A, González Herrero A, Ponce S. Evaluation of statistical illiteracy in Latin American clinicians and the piloting evaluation of a short course across multiple timepoints. BMC Med Educ 2022;22(1):54. https://doi.org/10.1186/s12909-022-03128-w DOI: https://doi.org/10.1186/s12909-022-03128-w

Friederichs H, Birkenstein R, Becker JC, Marschall B, Weissenstein A. Risk literacy assessment of general practitioners and medical students using the Berlin Numeracy Test. BMC Fam Pract 2020;21(1):143. https://doi.org/10.1186/s12875-020-01214-w DOI: https://doi.org/10.1186/s12875-020-01214-w

Rothman RL, Montori VM, Cherrington A, Pignone MP. Perspective: The Role of Numeracy in Health Care. J Health Commun 2008;13(6):583-95. https://doi.org/10.1080/10810730802281791 DOI: https://doi.org/10.1080/10810730802281791

Schmidt FM, Zottmann JM, Sailer M, Fischer MR, Berndt M. Statistical literacy and scientific reasoning & argumentation in physicians. MS J Med Educ 2021;38(4):Doc77. https://doi.org/10.3205/zma001473 DOI: https://doi.org/10.21203/rs.3.rs-20026/v1

Petrova D, Mas G, Navarrete G, Rodriguez TT, Ortiz PJ, Garcia-Retamero R. Cancer screening risk literacy of physicians in training: An experimental study. PLoS One 2018;14(7):e0218821. https://doi.org/10.1371/journal.pone.0218821 DOI: https://doi.org/10.1371/journal.pone.0218821

Jenny MA, Keller N, Gigerenzer G. Assessing minimal medical statistical literacy using the Quick Risk Test: a prospective observational study in Germany. BMJ Open 2018;8(8):e020847. http://doi.org/10.1136/bmjopen-2017-020847 DOI: https://doi.org/10.1136/bmjopen-2017-020847

Johnson TV, Abbasi A, Schoenberg ED, Kellum R, Speake LD, Spiker C, et al. Numeracy among trainees: are we preparing physicians for evidence-based medicine? J Surg Educ 2014;71(2):211-5. http://doi.org/10.1016/j.jsurg.2013.07.013 DOI: https://doi.org/10.1016/j.jsurg.2013.07.013

Sheridan SL, Pignone M. Numeracy and the medical student’s ability to interpret data. Eff Clin Pract 2002;5(1):35-40. PMID: 11874195

Friederichs H, Schölling M, Marschall B, Weissenstein A. Assessment of risk literacy among german medical students: a cross-sectional study evaluating numeracy skills. HERA 2014;20(4):1139-47. https://doi.org/10.1080/10807039.2013.821909 DOI: https://doi.org/10.1080/10807039.2013.821909

Ghosh AK, Ghosh K. Translating evidence-based information into effective risk communication: Current challenges and opportunities. J Lab Clin Med 2005;145(4):171-80. https://doi.org/10.1016/j.lab.2005.02.006 DOI: https://doi.org/10.1016/j.lab.2005.02.006

Petrova D, Kostopoulou O, Delaney BC, Cokely ET, Garcia-Retamero R. Strengths and gaps in physicians’ risk communication: a scenario study of the influence of numeracy on cancer screening communication. Med Decis Making 2018;38(3):355-365. https://doi.org/10.1177/0272989X17729359 DOI: https://doi.org/10.1177/0272989X17729359

Gigerenzer G, Gaissmaier W, Kurz-Milcke E, Schwartz LM, Woloshin S. Helping doctors and patients make sense of health statistics. Psychol Sci Public Interest 2007;8(2):53-96. https://doi.org/10.1111/j.1539-6053.2008.00033.x DOI: https://doi.org/10.1111/j.1539-6053.2008.00033.x

Bramwell R, West H, Salmon P. Health professionals’ and service users’ interpretation of screening test results: experimental study. BMJ 2006;333(7562):284. https://doi.org/10.1136/bmj.38884.663102.AE DOI: https://doi.org/10.1136/bmj.38884.663102.AE

Wegwarth O. Do physicians understand cancer screening statistics? A national survey of primary care physicians in the united states. Ann Intern Med 2012;156(5):340-9. https://doi.org/10.7326/0003-4819-156-5-201203060-00005 DOI: https://doi.org/10.7326/0003-4819-156-5-201203060-00005

Cokely ET, Galesic M, Schulz E, Ghazal S, Garcia-Retamero R. Measuring risk literacy: the berlin numeracy test. Juldgm Decis Mak 2012;7(1):25-47. https://doi.org/10.1017/S1930297500001819 DOI: https://doi.org/10.1017/S1930297500001819

Eddy D. Probabilistic reasoning in clinical medicine: Problems and opportunities. In: Kahneman D, Slovic P,. Tversky A, eds. Judgment under Uncertainty: Heuristics and Biases. Cambridge: Cambridge University Press; 1982. p. 249-67. https://doi.org/10.1017/CBO9780511809477.019 DOI: https://doi.org/10.1017/CBO9780511809477.019

Anderson BL, Gigerenzer G, Parker S, Schulkin J. Statistical literacy in obstetricians and gynecologists. J Healthc Qual 2014;36(1):5-17. https://doi.org/10.1111/j.1945-1474.2011.00194.x DOI: https://doi.org/10.1111/j.1945-1474.2011.00194.x

Schwartz LM. The role of numeracy in understanding the benefit of screening mammography. Ann Intern Med 1997;127(11):966-72. https://doi.org/10.7326/0003-4819-127-11-199712010-00003 DOI: https://doi.org/10.7326/0003-4819-127-11-199712010-00003

Chapman GB, Liu J. Numeracy, frequency, and Bayesian reasoning. Juldgm Decis Mak 2009;4(1):34-40. https://doi.org/10.1017/S1930297500000681 DOI: https://doi.org/10.1017/S1930297500000681

Garcia-Retamero R, Hoffrage U. Visual representation of statistical information improves diagnostic inferences in doctors and their patients. Soc Sci Med 2013:83:27-33. https://doi.org/10.1016/j.socscimed.2013.01.034 DOI: https://doi.org/10.1016/j.socscimed.2013.01.034

Weber P, Binder K, Krauss S. Why can only 24% solve bayesian reasoning problems in natural frequencies: frequency phobia in spite of probability blindness. Front Psychol 2018;9:1833. https://doi.org/10.3389/fpsyg.2018.01833 DOI: https://doi.org/10.3389/fpsyg.2018.01833

Brase GL. Pictorial representations in statistical reasoning. Appl Cogn Psychol 2009;23(3):369-81. https://doi.org/10.1002/acp.1460 DOI: https://doi.org/10.1002/acp.1460

Ottley A, Peck EM, Harrison LT, Afergan D, Ziemkiewicz C, Taylor HA, et al. Improving Bayesian reasoning: the effects of phrasing, visualization, and spatial ability. EEE Trans Vis Comput Graph 2016;22(1):529-38. https://doi.org/10.1109/TVCG.2015.2467758 DOI: https://doi.org/10.1109/TVCG.2015.2467758

Binder K, Krauss S, Bruckmaier G. Effects of visualizing statistical information – an empirical study on tree diagrams and 2 × 2 tables. Front Psychol 2015;6:1186. https://doi.org/10.3389/fpsyg.2015.01186 DOI: https://doi.org/10.3389/fpsyg.2015.01186

Reani M, Davies A, Peek N, Jay C. Evidencing how experience and problem format affect probabilistic reasoning through interaction analysis. Front Psychol 2019;10:1548. https://doi.org/10.3389/fpsyg.2019.01548 DOI: https://doi.org/10.3389/fpsyg.2019.01548

Bilalić M, McLeod P, Gobet F. Why good thoughts block better ones: The mechanism of the pernicious Einstellung (set) effect. Cognition 2008;108(3):652-61. https://doi.org/10.1016/j.cognition.2008.05.005 DOI: https://doi.org/10.1016/j.cognition.2008.05.005

Published

2023-12-05

How to Cite

1.
Moreira LM, Furukawa FY, Santos I, Gusso GDF. Risk literacy assessment of medical students in Brazil. Rev Bras Med Fam Comunidade [Internet]. 2023 Dec. 5 [cited 2024 Jul. 3];18(45):3802. Available from: https://rbmfc.org.br/rbmfc/article/view/3802

Issue

Section

Especial Residência Médica

Plaudit