Natural Frequencies Improve Public Understanding of Medical Test Results: An Experimental Study on Various Bayesian Inference Tasks with Multiple Scoring Methods and Non-Bayesian Reasoning Strategies

IF 3.1 3区 医学 Q2 HEALTH CARE SCIENCES & SERVICES
Soyun Kim
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引用次数: 0

Abstract

BackgroundIt is well established that the natural frequencies (NF) format is cognitively more beneficial for Bayesian inference than the conditional probabilities (CP) format. However, empirical studies have suggested that the NF facilitation effect might be limited to specific groups of individuals. Unlike previous studies that focused on a limited number of Bayesian inference problems evaluated by a single scoring method, it was essential to examine multiple Bayesian problems using various scoring metrics. This study also explored the impact of numeracy on Bayesian inference and assessed non-Bayesian cognitive strategies using the numerical information in problem solving.MethodsIn a Web-based experimental survey, 175 South Korean adults were randomly assigned to 1 of 2 format groups (NF v. CP). After completing numeracy scales, participants were asked to estimate 4 Bayesian inference problems and document the numerical information used in their problem-solving process. Four scoring methods—strict rounding, loose rounding, absolute deviation, and 50-Split—were used to evaluate participants’ estimations.ResultsThe NF format generally outperformed the CP format across all problems, except in a chorionic villus sampling test problem when evaluated using the 50-Split method. In addition, numeracy levels significantly influenced Bayesian inference; participants with higher numeracy demonstrated better performance. In addition, participants used various non-Bayesian strategies influenced by the format and the nature of the problems.ConclusionsThe NF facilitation effect was consistently observed across multiple Bayesian problems and scoring methods. Individuals with higher numeracy levels benefited more from the NF format. The use of various non-Bayesian strategies varied with the formats and nature of specific tasks.HighlightsThe natural frequencies (NF) format is known to foster understanding of medical test results compared with the conditional probabilities (CP) format, but some studies have reported that this benefit is either nonexistent or limited to specific groups. This study aims to replicate previous empirical studies using various Bayesian problems using multiple scoring methods. The NF format fosters understanding of medical test results across all Bayesian problems by all scoring methods, except in the CVS problem when using a 50-Split scoring method. Participants with high numeracy perform better Bayesian inference than those with lower numeracy. Particularly, higher numerates benefit more in the NF format than lower numerates do. In addition, the public tend to use various non-Bayesian reasoning strategies depending on the format and the nature of the tasks.
自然频率提高了公众对医学检验结果的理解:使用多种评分方法和非贝叶斯推理策略完成各种贝叶斯推理任务的实验研究
背景众所周知,自然频率(NF)格式比条件概率(CP)格式更有利于贝叶斯推理。然而,实证研究表明,自然频率的促进作用可能仅限于特定人群。以往的研究侧重于用单一评分方法评估数量有限的贝叶斯推理问题,与此不同的是,本研究必须使用各种评分标准考察多个贝叶斯问题。本研究还探讨了计算能力对贝叶斯推理的影响,并评估了在解决问题时使用数字信息的非贝叶斯认知策略。方法在一项基于网络的实验调查中,175 名韩国成年人被随机分配到两种形式组(NF 组和 CP 组)中的一种。在完成计算量表后,参与者被要求估计 4 个贝叶斯推理问题,并记录解决问题过程中使用的数字信息。结果在所有问题上,NF格式的成绩普遍优于CP格式,但在使用50-Split方法评估绒毛取样测试问题时除外。此外,计算能力水平对贝叶斯推理有很大影响;计算能力越高的参与者表现越好。此外,受问题形式和性质的影响,参与者还使用了各种非贝叶斯策略。计算水平较高的人从 NF 形式中获益更多。亮点众所周知,与条件概率(CP)格式相比,自然频率(NF)格式可促进对医学测试结果的理解,但一些研究报告称,这种益处要么不存在,要么仅限于特定群体。本研究旨在利用多种计分方法,使用各种贝叶斯问题,重复以往的实证研究。在所有贝叶斯问题中,NF格式在所有计分方法下都能促进对医学测试结果的理解,但在CVS问题中使用50-Split计分法时除外。计算能力高的学员比计算能力低的学员能更好地进行贝叶斯推理。特别是,高运算能力者比低运算能力者在 NF 形式中获益更多。此外,根据任务的形式和性质,公众倾向于使用各种非贝叶斯推理策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Medical Decision Making
Medical Decision Making 医学-卫生保健
CiteScore
6.50
自引率
5.60%
发文量
146
审稿时长
6-12 weeks
期刊介绍: Medical Decision Making offers rigorous and systematic approaches to decision making that are designed to improve the health and clinical care of individuals and to assist with health care policy development. Using the fundamentals of decision analysis and theory, economic evaluation, and evidence based quality assessment, Medical Decision Making presents both theoretical and practical statistical and modeling techniques and methods from a variety of disciplines.
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