Context Matters: Assessing Cognitive Bias Susceptibility in Health Care Professionals Using Generic and Context-Specific Scales

Context Matters: Assessing Cognitive Bias Susceptibility in Health Care Professionals Using Generic and Context-Specific Scales | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Context Matters: Assessing Cognitive Bias Susceptibility in Health Care Professionals Using Generic and Context-Specific Scales Miyuki Takase This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5666472/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigated whether healthcare professionals exhibit differing levels of susceptibility to cognitive biases when responding to generic versus context-specific cognitive bias scales. Fifty-five nurses from three healthcare institutions in Japan completed an online survey assessing three biases: conjunction fallacy, base-rate neglect, and belief bias. Generic scales presented abstract scenarios, while context-specific scales utilised clinical situations relevant to patient fall risk. Results revealed that nurses demonstrated significantly higher susceptibility to cognitive biases on context-specific scales for conjunction fallacy and belief bias, although no significant difference was observed for base-rate neglect. These findings suggest that professional expertise may activate cognitive shortcuts, such as pattern recognition, leading to biased judgments in domain-specific contexts. The study underscores the importance of employing context-specific measures to accurately assess the impact of cognitive biases on professional decision-making. This approach is critical for advancing research on expert judgement and mitigating errors in healthcare practice. Psychology Cognitive bias Expert judgement Generic scale Context-specific scale Nurses Introduction Health care professionals, like individuals in other fields, are susceptible to cognitive biases—systematic tendencies that distort information processing, leading to inaccurate judgements and decisions (Crowley et al., 2013 ); Korteling et al. ( 2023 ). Studies have demonstrated that cognitive biases contribute to diagnostic errors by physicians (Jala et al., 2023 ; Saposnik et al., 2016 ; Watari et al., 2022 ) and suboptimal decision-making by nurses (Martin et al., 2022 ), significantly affecting patient care. Therefore, understanding their susceptibility to cognitive bias is an important first step to prevent medical errors. However, many existing cognitive bias scales are generic and involve scenarios unrelated to professional contexts (e.g., see the sample items in the generic scales in Table 1 ). A biased response to such a question does not necessarily indicate that health care professionals would exhibit similar biases in their clinical practice. Health care professionals might respond correctly in Mr. Tanaka’s scenario shown in Table 1 (see the context-specific conjunction fallacy sample item) because their expertise allows them to avoid certain biases (e.g., recognising that forgetfulness is common in older adults without dementia and that stroke risk increases with age). Conversely, this same expertise might activate cognitive shortcuts (e.g., intuitively associating forgetfulness with dementia), leading to biased decisions. Table 1 Definitions and sample items in the cognitive bias scales Definitions Sample items Generic scales Context-specific scales Conjunction fallacy: the erroneous belief that a conjunction is more likely than one of its constituents (Tversky & Kahneman, 1983 ). Jamal is 21 and lives near Brooklyn. He has dreadlocks and drives a convertible. He is 6' 7” and very athletic. Which statement is most likely? 1. Jamal is a gymnast, 2. Jamal is a gymnast and a basketball player. (Šrol, 2022 ) At 2:00 p.m., a patient in the same room as Mr. Tanaka pressed a call bell and told a nurse that Mr. Tanaka had fallen at his bedside. Mr. Tanaka, aged 88 years, sometimes forgets to take his medication and sometimes gets lost on the way to the x-ray room. Which is more likely? 1. Ms. Tanaka has a stroke. 2. Ms. Tanaka has a stroke and dementia. Base-rate neglect: the tendency to ignore underlying incidence rates, prior probabilities, or base rates, either by inflating or reducing them (Croskerry, 2003 ). Among the 1000 people that participated in the study, there were 995 nurses and five doctors. John is a randomly chosen participant in this research. He is 34 years old. He lives in a nice house in a fancy neighbourhood. He expresses himself nicely and is very interested in politics. He invests a lot of time in his career. Which is more likely? a) John is a nurse. b) John is a doctor. (Erceg et al., 2022 ) In ward A, 100 patients fall down every year, of which 97.5% have muscle weakness and 2.5% have side effects of psychotropic drugs. 92-year-old Ms. Nakamura is a randomly selected patient in ward A. She usually takes a sleeping pill before going to sleep, but last night she took one more pill because she could not sleep. Perhaps because of this, she seems to be in a daze in the morning. Which is more likely? a) Ms. Nakamura falls over due to side effects of psychotropic drugs. b) Ms. Nakamura has muscle weakness. Belief bias: the tendency to accept or reject data depending on one’s personal belief system, especially when the focus is on the conclusion and not the premises or data (Croskerry & Ryle, 2019 ). Premise 1: All Eastern countries are communist. Premise 2: Canada is not an Eastern country. Conclusion: Canada is not communist. (Erceg et al., 2022 ) Premise 1: Patients with cognitive decline are at higher risk for falls. Premise 2: Ms. B, who is 20 years old, does not have cognitive decline. Conclusion: Therefore, Ms. B is at low risk of falling. [Table 1 about here] It is critical to examine whether professionals respond similarly to generic and context-specific cognitive bias problems. Such investigations could clarify the validity of using generic scales to explore relationships between susceptibility to cognitive bias and its impact on professional practice. This study aimed to investigate whether differences exist in health care professionals’ responses to generic versus context-specific cognitive bias scales. Methods Study Design This cross-sectional study is part of a larger project investigating nurses’ judgements on patient fall risk and associated factors, including cognitive biases. While the present paper focuses on the findings related to the research question, other results will be reported separately. Participants Fifty-five nurses from three health care institutions in western Japan participated in the study. Data Collection Participants completed an online survey measuring three cognitive biases—conjunction fallacy, base-rate neglect, and belief bias—using both generic and context-specific instruments (see definitions and sample items in Table 1 ). These biases were selected for their relevance to probability forecasting and logical reasoning, both of which are critical for assessing patient fall risk—a leading cause of adverse events in health care. The generic scale included 14 items (6 for conjunction fallacy (Šrol, 2022 ), 4 for base-rate neglect, and 4 for belief bias (Erceg et al., 2022 )), each requiring a choice between a biased and a non-biased response. Context-specific scales, developed and pilot-tested with academic and practicing nurses, comprised 10 items (4 for conjunction fallacy, 3 each for base-rate neglect and belief bias) using the same response format. Responses indicating bias were scored as 1 and aggregated for each scale. Analysis Scores for each scale were normalised (ranging from 0 to 1) to facilitate comparison. The Wilcoxon signed-rank test was employed to compare scores between the generic and context-specific scales for conjunction fallacy, base-rate neglect, and belief bias, as the distributions of scores were skewed. Pairwise deletion was used for cases with missing responses. Ethical Considerations Ethical approval was obtained from the institutional review board before data collection. Results Descriptive statistics and Spearman correlation coefficients are presented in Table 2 , along with the Kuder-Richardson 20 (KR20) coefficients for each scale, except for the context-specific conjunction fallacy scale. For this scale, constant responses on two items (i.e., all participants selected biased responses) precluded reliable KR20 calculation. Table 2 Descriptive statistics and spearman correlation Mean # St.Dev KR20 CF_CS CF_G BN_CS BN_G BB_CS Conjunction fallacy: Context-specific (CF_CS) 0.967 0.016 - # Conjunction fallacy: Generic (CF_G) 0.820 0.039 0.830 0.117 Base-rate neglect: Context-specific (BN_CS) 0.759 0.045 0.699 -0.076 0.157 Base-rate neglect: Generic (BN_G) 0.667 0.049 0.783 0.060 0.255 0.355 Belief bias: Context-specific (BB_CS) 0.686 0.041 0.649 -0.044 -0.053 0.098 0.145 Belief bias: Generic (BB_G) 0.577 0.053 0.816 0.167 0.060 0.121 0.120 0.413 Note: # KR20 could not be calculated for the context-specific conjunction fallacy scale, as two of the items had constant responses (i.e., all participants selected biased responses). As shown in Table 2 , nurses consistently scored higher (indicating greater susceptibility to biases) on the context-specific scales compared to the generic scales. Notably, they exhibited strong susceptibility to conjunction fallacy, with mean scores of 0.97 for the context-specific scale and 0.82 for the generic scale. [Table 2 about here] The Wilcoxon signed-rank test revealed a significantly higher total score for the context-specific conjunction fallacy scale compared to its generic counterpart (z = 3.425, p = 0.0006). A similar trend was observed for the belief bias scale (z = 2.107, p = 0.0351). In contrast, the difference between the context-specific and generic scales for base-rate neglect was not statistically significant (z = 1.561, p = 0.1186). These findings suggest that professionals, such as nurses, may exhibit differing levels of susceptibility when responding to generic versus context-specific scales. Discussion This study examined whether healthcare professionals respond differently to generic versus context-specific cognitive bias scales. The findings indicate that nurses exhibit more biased responses on context-specific scales compared to generic ones. Previous research suggests that expert knowledge enhances judgement accuracy (Dijkstra et al., 2013 ) through the effective use of heuristics (Corrao & Argano, 2022 ). However, experts are also prone to cognitive biases (Wilson et al., 2020 ) as their expertise and professional experience often lead to selective attention and reliance on heuristics (Dror, 2020 ), increasing the likelihood of errors. In particular, medical professionals frequently base diagnostic decisions on pattern recognition, which develops through repeated exposure to similar situations (Croskerry, 2009 ). Consequently, the patient scenarios presented in the context-specific scales may have triggered pattern recognition and activated System 1 thinking, resulting in incorrect judgements that violated statistical probability and logical reasoning. This study offers two key implications. First, professionals may be more vulnerable to cognitive biases when addressing domain-specific problems compared to general ones. Second, as Berthet ( 2021 ) argues, research on expert judgement and decision-making must employ measures tailored to the specific contexts in which decisions are made. A larger-scale experimental study is needed to strengthen these findings. Conclusion Healthcare professionals may exhibit varying levels of susceptibility to cognitive bias when evaluating generic versus context-specific problems. To accurately assess the impact of cognitive bias on their judgement accuracy, the use of context-specific measures is essential. Declarations Competing interest The author declares no competing interests. Funding sources This study was supported by JSPS KAKENHI Grant Numbers 22K10749. References Berthet, V. (2021). The measurement of individual differences in cognitive biases: A review and improvement. Front Psychol , 12 , 630177. https://doi.org/10.3389/fpsyg.2021.630177 Corrao, S., & Argano, C. (2022). Rethinking clinical decision-making to improve clinical reasoning. Front Med (Lausanne) , 9 , 900543. https://doi.org/10.3389/fmed.2022.900543 Croskerry, P. (2003). The importance of cognitive errors in diagnosis and strategies to minimize them. Acad Med , 78 (8), 775-780. https://doi.org/10.1097/00001888-200308000-00003 Croskerry, P. (2009). A universal model of diagnostic reasoning. Acad Med , 84 (8), 1022-1028. https://doi.org/10.1097/ACM.0b013e3181ace703 Croskerry, P., & Ryle, C. A. (2019). Croskerry’s List of 50 Common Biases: 50 Cognitive and Affective Biases in Medicine (Alphabetically). In Risk and Reasoning in Clinical Diagnosis (pp. 177-184). Oxford University Press. https://doi.org/10.1093/med/9780190944001.005.0001 Crowley, R. S., Legowski, E., Medvedeva, O., Reitmeyer, K., Tseytlin, E., Castine, M., Jukic, D., & Mello-Thoms, C. (2013). Automated detection of heuristics and biases among pathologists in a computer-based system. Advances in Health Sciences Education , 18 (3), 343-363. https://doi.org/10.1007/s10459-012-9374-z Dijkstra, K. A., van der Pligt, J., & van Kleef, G. A. (2013). Deliberation versus intuition: Decomposing the role of expertise in judgment and decision making. Journal of Behavioral Decision Making , 26 (3), 285-294. https://doi.org/https://doi.org/10.1002/bdm.1759 Dror, I. E. (2020). Cognitive and human factors in expert decision making: Six fallacies and the eight sources of bias. Anal Chem , 92 (12), 7998-8004. https://doi.org/10.1021/acs.analchem.0c00704 Erceg, N., Galic, Z., & Bubić, A. (2022). Normative responding on cognitive bias tasks: Some evidence for a weak rationality factor that is mostly explained by numeracy and actively open-minded thinking. Intelligence , 90 , 101619. https://doi.org/10.1016/j.intell.2021.101619 Jala, S., Fry, M., & Elliott, R. (2023). Cognitive bias during clinical decision-making and its influence on patient outcomes in the emergency department: A scoping review. Journal of Clinical Nursing , 32 (19-20), 7076-7085. https://doi.org/https://doi.org/10.1111/jocn.16845 Korteling, J. E., Paradies, G., & Sassen-van Meer, J. (2023). Cognitive bias and how to improve sustainable decision making. Frontiers in Psychology , 14 , 1129835. https://doi.org/10.3389/fpsyg.2023.1129835 Martin, K., Bickle, K., & Lok, J. (2022). Investigating the impact of cognitive bias in nursing documentation on decision-making and judgement. International Journal of Mental Health Nursing , 31 (4), 897-907. https://doi.org/https://doi.org/10.1111/inm.12997 Saposnik, G., Redelmeier, D., Ruff, C. C., & Tobler, P. N. (2016). Cognitive biases associated with medical decisions: a systematic review. BMC Medical Informatics and Decision Making , 16 (1), 138. https://doi.org/10.1186/s12911-016-0377-1 Šrol, J. (2022). Individual differences in epistemically suspect beliefs: The role of analytic thinking and susceptibility to cognitive biases. Thinking & Reasoning , 28 (1), 125-162. https://doi.org/10.1080/13546783.2021.1938220 Tversky, A., & Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment [Article]. Psychological Review , 90 (4), 293-315. https://doi.org/10.1037/0033-295X.90.4.293 Watari, T., Tokuda, Y., Amano, Y., Onigata, K., & Kanda, H. (2022). Cognitive Bias and Diagnostic Errors among Physicians in Japan: A Self-Reflection Survey. International Journal of Environmental Research and Public Health , 19 (8), 4645. https://www.mdpi.com/1660-4601/19/8/4645 Wilson, C. G., Shipley, T. F., & Davatzes, A. K. (2020). Evidence of vulnerability to decision bias in expert field scientists. Applied Cognitive Psychology , 34 (5), 1217-1223. https://doi.org/https://doi.org/10.1002/acp.3677 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5666472","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":391717006,"identity":"34552766-a152-4bd9-a5c3-8eac6d80d3a6","order_by":0,"name":"Miyuki Takase","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYFACNhBhA2ZKwMQM8GnggWhJI13LYVQteIE9+7HEjz8qzif2958xvPEDqJe//QBDcQE+W3jSDkvznLmdOONGjrFlD1CLxJkEBuMZeB2W3iDN2HY7seEGj5kE7z+gC28wMBjz4NPC/7z5589/5xLnnz9jJvkHaIs8QS0SacckeBsOJG44kGMmzQPUYkBQy41nadY8x5KNN95IK7aWYUjnMTyT2IDXL+z9acY3f9TYyc47f3jjzTcM1nJyxw8fM8YXYjDg2ACzloGBsc2YCB0M9sgc5sfEaBkFo2AUjIIRAwDCf0lRNnMYQgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3930-9828","institution":"Yasuda Women's University","correspondingAuthor":true,"prefix":"","firstName":"Miyuki","middleName":"","lastName":"Takase","suffix":""}],"badges":[],"createdAt":"2024-12-18 06:16:54","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-5666472/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5666472/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":71949856,"identity":"c3757009-aa24-43d3-a2d7-aeb0401357f7","added_by":"auto","created_at":"2024-12-20 04:09:03","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":319438,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5666472/v1/70c1c9ce-9b8c-4e62-8756-ea9ef95a99c0.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eContext Matters: Assessing Cognitive Bias Susceptibility in Health Care Professionals Using Generic and Context-Specific Scales\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHealth care professionals, like individuals in other fields, are susceptible to cognitive biases\u0026mdash;systematic tendencies that distort information processing, leading to inaccurate judgements and decisions (Crowley et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e); Korteling et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Studies have demonstrated that cognitive biases contribute to diagnostic errors by physicians (Jala et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Saposnik et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Watari et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) and suboptimal decision-making by nurses (Martin et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), significantly affecting patient care. Therefore, understanding their susceptibility to cognitive bias is an important first step to prevent medical errors.\u003c/p\u003e \u003cp\u003eHowever, many existing cognitive bias scales are generic and involve scenarios unrelated to professional contexts (e.g., see the sample items in the generic scales in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A biased response to such a question does not necessarily indicate that health care professionals would exhibit similar biases in their clinical practice. Health care professionals might respond correctly in Mr. Tanaka\u0026rsquo;s scenario shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (see the context-specific conjunction fallacy sample item) because their expertise allows them to avoid certain biases (e.g., recognising that forgetfulness is common in older adults without dementia and that stroke risk increases with age). Conversely, this same expertise might activate cognitive shortcuts (e.g., intuitively associating forgetfulness with dementia), leading to biased decisions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDefinitions and sample items in the cognitive bias scales\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDefinitions\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eSample items\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneric scales\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContext-specific scales\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConjunction fallacy:\u003c/p\u003e \u003cp\u003ethe erroneous belief that a conjunction is more likely than one of its constituents (Tversky \u0026amp; Kahneman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e1983\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJamal is 21 and lives near Brooklyn. He has dreadlocks and drives a convertible. He is 6' 7\u0026rdquo; and very athletic. Which statement is most likely?\u003c/p\u003e \u003cp\u003e1. Jamal is a gymnast,\u003c/p\u003e \u003cp\u003e2. Jamal is a gymnast and a basketball player. (Šrol, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAt 2:00 p.m., a patient in the same room as Mr. Tanaka pressed a call bell and told a nurse that Mr. Tanaka had fallen at his bedside. Mr. Tanaka, aged 88 years, sometimes forgets to take his medication and sometimes gets lost on the way to the x-ray room. Which is more likely?\u003c/p\u003e \u003cp\u003e1. Ms. Tanaka has a stroke.\u003c/p\u003e \u003cp\u003e2. Ms. Tanaka has a stroke and dementia.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase-rate neglect:\u003c/p\u003e \u003cp\u003ethe tendency to ignore underlying incidence rates, prior probabilities, or base rates, either by inflating or reducing them (Croskerry, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAmong the 1000 people that participated in the study, there were 995 nurses and five doctors. John is a randomly chosen participant in this research. He is 34 years old. He lives in a nice house in a fancy neighbourhood. He expresses himself nicely and is very interested in politics. He invests a lot of time in his career. Which is more likely?\u003c/p\u003e \u003cp\u003ea) John is a nurse.\u003c/p\u003e \u003cp\u003eb) John is a doctor. (Erceg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIn ward A, 100 patients fall down every year, of which 97.5% have muscle weakness and 2.5% have side effects of psychotropic drugs. 92-year-old Ms. Nakamura is a randomly selected patient in ward A. She usually takes a sleeping pill before going to sleep, but last night she took one more pill because she could not sleep. Perhaps because of this, she seems to be in a daze in the morning. Which is more likely?\u003c/p\u003e \u003cp\u003ea) Ms. Nakamura falls over due to side effects of psychotropic drugs.\u003c/p\u003e \u003cp\u003eb) Ms. Nakamura has muscle weakness.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelief bias:\u003c/p\u003e \u003cp\u003ethe tendency to accept or reject data depending on one\u0026rsquo;s personal belief system, especially when the focus is on the conclusion and not the premises or data (Croskerry \u0026amp; Ryle, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePremise 1: All Eastern countries are communist.\u003c/p\u003e \u003cp\u003ePremise 2: Canada is not an Eastern country.\u003c/p\u003e \u003cp\u003eConclusion: Canada is not communist. (Erceg et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePremise 1: Patients with cognitive decline are at higher risk for falls.\u003c/p\u003e \u003cp\u003ePremise 2: Ms. B, who is 20 years old, does not have cognitive decline.\u003c/p\u003e \u003cp\u003eConclusion: Therefore, Ms. B is at low risk of falling.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eIt is critical to examine whether professionals respond similarly to generic and context-specific cognitive bias problems. Such investigations could clarify the validity of using generic scales to explore relationships between susceptibility to cognitive bias and its impact on professional practice. This study aimed to investigate whether differences exist in health care professionals\u0026rsquo; responses to generic versus context-specific cognitive bias scales.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy Design\u003c/h2\u003e\n \u003cp\u003eThis cross-sectional study is part of a larger project investigating nurses\u0026rsquo; judgements on patient fall risk and associated factors, including cognitive biases. While the present paper focuses on the findings related to the research question, other results will be reported separately.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eFifty-five nurses from three health care institutions in western Japan participated in the study.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eParticipants completed an online survey measuring three cognitive biases\u0026mdash;conjunction fallacy, base-rate neglect, and belief bias\u0026mdash;using both generic and context-specific instruments (see definitions and sample items in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). These biases were selected for their relevance to probability forecasting and logical reasoning, both of which are critical for assessing patient fall risk\u0026mdash;a leading cause of adverse events in health care.\u003c/p\u003e\n\u003cp\u003eThe generic scale included 14 items (6 for conjunction fallacy (\u0026Scaron;rol, \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e), 4 for base-rate neglect, and 4 for belief bias (Erceg et al., \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e)), each requiring a choice between a biased and a non-biased response. Context-specific scales, developed and pilot-tested with academic and practicing nurses, comprised 10 items (4 for conjunction fallacy, 3 each for base-rate neglect and belief bias) using the same response format. Responses indicating bias were scored as 1 and aggregated for each scale.\u003c/p\u003e\n\u003ch3\u003eAnalysis\u003c/h3\u003e\n\u003cp\u003eScores for each scale were normalised (ranging from 0 to 1) to facilitate comparison. The Wilcoxon signed-rank test was employed to compare scores between the generic and context-specific scales for conjunction fallacy, base-rate neglect, and belief bias, as the distributions of scores were skewed. Pairwise deletion was used for cases with missing responses.\u003c/p\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003eEthical approval was obtained from the institutional review board before data collection.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptive statistics and Spearman correlation coefficients are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, along with the Kuder-Richardson 20 (KR20) coefficients for each scale, except for the context-specific conjunction fallacy scale. For this scale, constant responses on two items (i.e., all participants selected biased responses) precluded reliable KR20 calculation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics and spearman correlation\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSt.Dev\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKR20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCF_CS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCF_G\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBN_CS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBN_G\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eBB_CS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConjunction fallacy: Context-specific (CF_CS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e- \u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConjunction fallacy: Generic (CF_G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.830\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase-rate neglect: Context-specific (BN_CS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.759\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.699\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBase-rate neglect: Generic (BN_G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelief bias: Context-specific (BB_CS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelief bias: Generic (BB_G)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.577\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.816\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.413\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: \u003csup\u003e#\u003c/sup\u003eKR20 could not be calculated for the context-specific conjunction fallacy scale, as two of the items had constant responses (i.e., all participants selected biased responses).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, nurses consistently scored higher (indicating greater susceptibility to biases) on the context-specific scales compared to the generic scales. Notably, they exhibited strong susceptibility to conjunction fallacy, with mean scores of 0.97 for the context-specific scale and 0.82 for the generic scale.\u003c/p\u003e \u003cp\u003e[Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eThe Wilcoxon signed-rank test revealed a significantly higher total score for the context-specific conjunction fallacy scale compared to its generic counterpart (z\u0026thinsp;=\u0026thinsp;3.425, p\u0026thinsp;=\u0026thinsp;0.0006). A similar trend was observed for the belief bias scale (z\u0026thinsp;=\u0026thinsp;2.107, p\u0026thinsp;=\u0026thinsp;0.0351). In contrast, the difference between the context-specific and generic scales for base-rate neglect was not statistically significant (z\u0026thinsp;=\u0026thinsp;1.561, p\u0026thinsp;=\u0026thinsp;0.1186). These findings suggest that professionals, such as nurses, may exhibit differing levels of susceptibility when responding to generic versus context-specific scales.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined whether healthcare professionals respond differently to generic versus context-specific cognitive bias scales. The findings indicate that nurses exhibit more biased responses on context-specific scales compared to generic ones. Previous research suggests that expert knowledge enhances judgement accuracy (Dijkstra et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) through the effective use of heuristics (Corrao \u0026amp; Argano, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, experts are also prone to cognitive biases (Wilson et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) as their expertise and professional experience often lead to selective attention and reliance on heuristics (Dror, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), increasing the likelihood of errors. In particular, medical professionals frequently base diagnostic decisions on pattern recognition, which develops through repeated exposure to similar situations (Croskerry, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Consequently, the patient scenarios presented in the context-specific scales may have triggered pattern recognition and activated System 1 thinking, resulting in incorrect judgements that violated statistical probability and logical reasoning.\u003c/p\u003e \u003cp\u003eThis study offers two key implications. First, professionals may be more vulnerable to cognitive biases when addressing domain-specific problems compared to general ones. Second, as Berthet (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) argues, research on expert judgement and decision-making must employ measures tailored to the specific contexts in which decisions are made. A larger-scale experimental study is needed to strengthen these findings.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eHealthcare professionals may exhibit varying levels of susceptibility to cognitive bias when evaluating generic versus context-specific problems. To accurately assess the impact of cognitive bias on their judgement accuracy, the use of context-specific measures is essential.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by JSPS KAKENHI Grant Numbers 22K10749.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eBerthet, V. (2021). The measurement of individual differences in cognitive biases: A review and improvement. \u003cem\u003eFront Psychol\u003c/em\u003e,\u003cem\u003e\u0026nbsp;12\u003c/em\u003e, 630177. https://doi.org/10.3389/fpsyg.2021.630177\u003c/li\u003e\n \u003cli\u003eCorrao, S., \u0026amp; Argano, C. (2022). Rethinking clinical decision-making to improve clinical reasoning. \u003cem\u003eFront Med (Lausanne)\u003c/em\u003e,\u003cem\u003e\u0026nbsp;9\u003c/em\u003e, 900543. https://doi.org/10.3389/fmed.2022.900543\u003c/li\u003e\n \u003cli\u003eCroskerry, P. (2003). The importance of cognitive errors in diagnosis and strategies to minimize them. \u003cem\u003eAcad Med\u003c/em\u003e,\u003cem\u003e\u0026nbsp;78\u003c/em\u003e(8), 775-780. https://doi.org/10.1097/00001888-200308000-00003\u003c/li\u003e\n \u003cli\u003eCroskerry, P. (2009). A universal model of diagnostic reasoning. \u003cem\u003eAcad Med\u003c/em\u003e,\u003cem\u003e\u0026nbsp;84\u003c/em\u003e(8), 1022-1028. https://doi.org/10.1097/ACM.0b013e3181ace703\u003c/li\u003e\n \u003cli\u003eCroskerry, P., \u0026amp; Ryle, C. A. (2019). Croskerry\u0026rsquo;s List of 50 Common Biases: 50 Cognitive and Affective Biases in Medicine (Alphabetically). In \u003cem\u003eRisk and Reasoning in Clinical Diagnosis\u003c/em\u003e (pp. 177-184). Oxford University Press. https://doi.org/10.1093/med/9780190944001.005.0001\u003c/li\u003e\n \u003cli\u003eCrowley, R. S., Legowski, E., Medvedeva, O., Reitmeyer, K., Tseytlin, E., Castine, M., Jukic, D., \u0026amp; Mello-Thoms, C. (2013). Automated detection of heuristics and biases among pathologists in a computer-based system. \u003cem\u003eAdvances in Health Sciences Education\u003c/em\u003e,\u003cem\u003e\u0026nbsp;18\u003c/em\u003e(3), 343-363. https://doi.org/10.1007/s10459-012-9374-z\u003c/li\u003e\n \u003cli\u003eDijkstra, K. A., van der Pligt, J., \u0026amp; van Kleef, G. A. (2013). Deliberation versus intuition: Decomposing the role of expertise in judgment and decision making. \u003cem\u003eJournal of Behavioral Decision Making\u003c/em\u003e,\u003cem\u003e\u0026nbsp;26\u003c/em\u003e(3), 285-294. https://doi.org/https://doi.org/10.1002/bdm.1759\u003c/li\u003e\n \u003cli\u003eDror, I. E. (2020). Cognitive and human factors in expert decision making: Six fallacies and the eight sources of bias. \u003cem\u003eAnal Chem\u003c/em\u003e,\u003cem\u003e\u0026nbsp;92\u003c/em\u003e(12), 7998-8004. https://doi.org/10.1021/acs.analchem.0c00704\u003c/li\u003e\n \u003cli\u003eErceg, N., Galic, Z., \u0026amp; Bubić, A. (2022). Normative responding on cognitive bias tasks: Some evidence for a weak rationality factor that is mostly explained by numeracy and actively open-minded thinking. \u003cem\u003eIntelligence\u003c/em\u003e,\u003cem\u003e\u0026nbsp;90\u003c/em\u003e, 101619. https://doi.org/10.1016/j.intell.2021.101619\u003c/li\u003e\n \u003cli\u003eJala, S., Fry, M., \u0026amp; Elliott, R. (2023). Cognitive bias during clinical decision-making and its influence on patient outcomes in the emergency department: A scoping review. \u003cem\u003eJournal of Clinical Nursing\u003c/em\u003e,\u003cem\u003e\u0026nbsp;32\u003c/em\u003e(19-20), 7076-7085. https://doi.org/https://doi.org/10.1111/jocn.16845\u003c/li\u003e\n \u003cli\u003eKorteling, J. E., Paradies, G., \u0026amp; Sassen-van Meer, J. (2023). Cognitive bias and how to improve sustainable decision making. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;14\u003c/em\u003e, 1129835. https://doi.org/10.3389/fpsyg.2023.1129835\u003c/li\u003e\n \u003cli\u003eMartin, K., Bickle, K., \u0026amp; Lok, J. (2022). Investigating the impact of cognitive bias in nursing documentation on decision-making and judgement. \u003cem\u003eInternational Journal of Mental Health Nursing\u003c/em\u003e,\u003cem\u003e\u0026nbsp;31\u003c/em\u003e(4), 897-907. https://doi.org/https://doi.org/10.1111/inm.12997\u003c/li\u003e\n \u003cli\u003eSaposnik, G., Redelmeier, D., Ruff, C. C., \u0026amp; Tobler, P. N. (2016). Cognitive biases associated with medical decisions: a systematic review. \u003cem\u003eBMC Medical Informatics and Decision Making\u003c/em\u003e,\u003cem\u003e\u0026nbsp;16\u003c/em\u003e(1), 138. https://doi.org/10.1186/s12911-016-0377-1\u003c/li\u003e\n \u003cli\u003e\u0026Scaron;rol, J. (2022). Individual differences in epistemically suspect beliefs: The role of analytic thinking and susceptibility to cognitive biases. \u003cem\u003eThinking \u0026amp; Reasoning\u003c/em\u003e,\u003cem\u003e\u0026nbsp;28\u003c/em\u003e(1), 125-162. https://doi.org/10.1080/13546783.2021.1938220\u003c/li\u003e\n \u003cli\u003eTversky, A., \u0026amp; Kahneman, D. (1983). Extensional versus intuitive reasoning: The conjunction fallacy in probability judgment [Article]. \u003cem\u003ePsychological Review\u003c/em\u003e,\u003cem\u003e\u0026nbsp;90\u003c/em\u003e(4), 293-315. https://doi.org/10.1037/0033-295X.90.4.293\u003c/li\u003e\n \u003cli\u003eWatari, T., Tokuda, Y., Amano, Y., Onigata, K., \u0026amp; Kanda, H. (2022). Cognitive Bias and Diagnostic Errors among Physicians in Japan: A Self-Reflection Survey. \u003cem\u003eInternational Journal of Environmental Research and Public Health\u003c/em\u003e,\u003cem\u003e\u0026nbsp;19\u003c/em\u003e(8), 4645. https://www.mdpi.com/1660-4601/19/8/4645\u003c/li\u003e\n \u003cli\u003eWilson, C. G., Shipley, T. F., \u0026amp; Davatzes, A. K. (2020). Evidence of vulnerability to decision bias in expert field scientists. \u003cem\u003eApplied Cognitive Psychology\u003c/em\u003e,\u003cem\u003e\u0026nbsp;34\u003c/em\u003e(5), 1217-1223. https://doi.org/https://doi.org/10.1002/acp.3677\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"b3556482-2d47-4683-bfea-a4d80fce9902","identifier":"10.13039/501100001691","name":"Japan Society for the Promotion of Science","awardNumber":"22K10749","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Yasuda Women's University","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Cognitive bias, Expert judgement, Generic scale, Context-specific scale, Nurses","lastPublishedDoi":"10.21203/rs.3.rs-5666472/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5666472/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigated whether healthcare professionals exhibit differing levels of susceptibility to cognitive biases when responding to generic versus context-specific cognitive bias scales. Fifty-five nurses from three healthcare institutions in Japan completed an online survey assessing three biases: conjunction fallacy, base-rate neglect, and belief bias. Generic scales presented abstract scenarios, while context-specific scales utilised clinical situations relevant to patient fall risk. Results revealed that nurses demonstrated significantly higher susceptibility to cognitive biases on context-specific scales for conjunction fallacy and belief bias, although no significant difference was observed for base-rate neglect. These findings suggest that professional expertise may activate cognitive shortcuts, such as pattern recognition, leading to biased judgments in domain-specific contexts. The study underscores the importance of employing context-specific measures to accurately assess the impact of cognitive biases on professional decision-making. This approach is critical for advancing research on expert judgement and mitigating errors in healthcare practice.\u003c/p\u003e","manuscriptTitle":"Context Matters: Assessing Cognitive Bias Susceptibility in Health Care Professionals Using Generic and Context-Specific Scales","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-12-20 03:52:55","doi":"10.21203/rs.3.rs-5666472/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0a8968ad-d6d2-459f-a9af-b8efe33680c8","owner":[],"postedDate":"December 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":41752773,"name":"Psychology"}],"tags":[],"updatedAt":"2024-12-20T03:52:55+00:00","versionOfRecord":[],"versionCreatedAt":"2024-12-20 03:52:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5666472","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5666472","identity":"rs-5666472","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}