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International Journal of Healthcare Simulation
Bias in simulation training for healthcare professions: a scoping review

DOI:10.54531/zynx5861, Pages: 1-19
Article Type: Original Research, Article History


Bias potentially affects simulation-based training (SBT) for healthcare professions. The role bias plays in SBT design, presentations, and in the experiences of learners should be understood. Dual process theory is a well-accepted framework for understanding types of bias.


The authors performed a scoping review to map ‘bias’ in SBT of health professions in the literature. Search terms were developed for a query in the PubMed database. Researchers reviewed abstracts, met ten times to discuss which papers’ full texts to read, and then analysed and categorized the articles. Researchers used the Arksey and O’Malley framework for scoping reviews.


Three thousand six hundred and twenty abstracts were identified by a detailed query in the PubMed database of which, 115 full-text articles were identified for inclusion.


Articles published about bias in SBT cover a broad range of topics, from addressing how bias affects patient care, to bias in raters’ scoring of medical students on exams. Researchers found that the prevalence of articles on bias in SBT increased over time and focused primarily on implicit bias. Specific types of bias in some instances were difficult to identify, and several biases mentioned in papers were unique to this review. The results showed that many SBT methodologies (i.e. manikins, videos, etc.) were referenced in the papers. The type of simulation training most prevalent in the articles was simulated patient (SP) methodology. The results show that biases can be explored in any type of simulation method, indicating that simulationsists should be aware of bias in training during all types of training methodolgy.

Akturan, Binns-Calvey, and Park: Bias in simulation training for healthcare professions: a scoping review


Simulation-based training (SBT) for healthcare professions is increasingly used as an educational strategy and to improve patient safety [1–4]. SBT is an effective strategy to improve skills in healthcare professions [5]. Many different methodologies have been developed in SBT, and those methodologies have helped achieve learning outcomes, which leads to clinical competency [6]. Patient or human simulation is a well-known methodology involving human role players interacting with health professions’ education in a variety of experiential learning and assessment activities. The term simulated patient (SP) refers to a person trained to portray a role such as patients, clients, family members, healthcare professionals, etc. in realistic and repeatable method. The terms standardized patient and simulated patient are often used synonymously [7].

SBT should be developed and implemented to ensure that clinical competencies including technical, communication, decision-making and team dynamics, etc. are achieved [3]. Because SBT involves decision-making where learners must weigh different options to provide patient care, the role that bias plays in SBT design, presentations and in the experiences of learners should be understood [8].

Scoping reviews are useful when authors want to explore certain concepts in papers, and in the mapping, reporting or discussion of these concepts [9]. There are scoping reviews on SBT of healthcare professions exploring the types of professions engaged in interprofessional education, characterization of the types of simulations, effects of new technologies on SBT, effects of different methodologies on clinical competencies of healthcare professions and barriers to utilization different methodologies [10–12]. We did not find any scoping reviews on the topic of bias in SBT of healthcare professions.

In this review, we sought to explore bias in SBT of healthcare to: 1) identify which types of biases affect SBT for healthcare professionals, 2) categorize the types of bias explored and 3) note the prevalence of articles published on this topic.


We performed a scoping review to map ‘bias’ in the literature on SBT of health professions. Scoping reviews are used to examine the range and nature of the research activities, to determine the value of conducting a complete systematic review, to summarize and disseminate research findings, or to detect gaps in existing literature [13].

Review strategy

We used the Arksey and O’Malley framework for scoping reviews [13] which was developed and refined by Levac and colleagues [14]. This approach involves five steps:

1. Identifying the research question

SA and CP met to identify the focus of the scoping review: ‘How is the term “bias” in “simulation training” explored within the literature?’ After conducting background research, we discovered that the terms ‘cognitive bias’, ‘implicit bias’ and ‘decision-making’ are terms used in conjunction with ‘bias’, therefore it was decided to include these terms along with ‘simulation’ and ‘bias’ in the analysis.

2. Identifying relevant studies

After determining the scoping review goals and receiving assistance from a University of Illinois at Chicago-affiliated librarian, we decided to use a detailed query which included all potential MeSH terms and keywords that might be related to ‘simulation’ and ‘bias’ for a database search (Figure 1).

Query for database search.
Figure 1:

Query for database search.

We searched the PubMed, Medline and CINAHL databases with the same terms, and compared the results. The PubMed results were the most comprehensive and included the results from the other databases, so we decided to focus only on the PubMed database. We limited the results by publication language (English).

3. Selecting the studies

As a first step, researchers SA and ABC conducted a pilot study to determine the method for analyzing the papers for this scoping review. We reviewed the first 100 papers found by a search using the detailed query to determine which articles should be included in the review. We then compared notes on the abstracts and full texts of the papers. We decided to include only primary research articles as it was too difficult to evaluate review papers based on the aims of this scoping review. After this pilot, we decided to select articles for study inclusion based on the following criteria:

    (a) studies that investigated bias in simulation training of any health professions education program,

    (b) studies that investigated the role of bias in simulation training,

    (c) original articles, brief reports,

    (d) studies in which outcomes/assessment focused on decision-making.

The following exclusion criteria were also defined as:

    (a) any type of reviews,

    (b) studies written in a language other than English,

    (c) studies that did not include any simulation training, and

    (d) studies including bias in simulation training, but, without any explanation for bias.

SA and ABC decided to analyze the papers’ abstracts for first reading because it was determined that papers might be selected based on their abstracts (without reading the full text) using the inclusion criteria. SA and ABC independently reviewed all abstracts published up to August 31, 2020. We then discussed any discrepancies and reached a consensus on which articles to include for the full review (second stage of scoping review).

Classification of bias

We referred to the papers’ descriptions of the type of bias they addressed, to identify if the bias was implicit or cognitive. In instances where the type of bias was not specified in the paper, we identified the type of bias from the content of the paper, including instances where both cognitive and implicit bias were explored. We then further classified the specific type of bias, again referring to the article’s content. In cases where the bias was the same, but terminology differed between papers (i.e. one paper used the term ‘race bias’, while another referred to is as ‘racial bias’), we standardized the naming of the bias by choosing one term for a similar type of bias.

4. Charting the data

We used Arksey and O’Malley’s ‘descriptive-analysis’ approach to data extraction, summarizing information from the selected articles and recording the data [13]. We also applied Levac and et al.’s recommendations for the data charting process and used an Excel sheet to analyze the selected articles [14]. By using this approach, the key information from the selected papers was charted under the headings: article name, author, journal, year, country, article type, population, details of simulation training and details of bias.


5. Collating, summarizing and reporting the results

Three thousand six hundred and twenty abstracts were identified from PubMed. The first reading was conducted by SA and ABC from May 4, 2020 to August 31, 2020. During this first reading, we met 10 times to discuss which papers should be added for the second step (reading full texts). We reviewed 238 selected papers for the second step, and 125 full-text articles were selected to be analysed from October 23, 2020 to January 12, 2021. We independently read and reviewed the included articles, and reconvened at six online meetings to discuss individual findings (Figure 2).

Results of search strategy and process of paper selection.
Figure 2:

Results of search strategy and process of paper selection.

From 1985 until 2020, the number of articles published on the topic of bias in simulation in medical professional training increased dramatically (Figure 3).

Classification by year.
Figure 3:

Classification by year.

We completed a review of articles published on bias in SBT for healthcare professionals. The articles reviewed cover a broad range of topics, from addressing how bias affects patient care, to bias in raters’ scoring of medical students on exams. We did not assess the methodological quality of the articles, but categorized them into four general themes: the type of healthcare profession, the method of simulation, whether the bias was cognitive or implicit, and the specific bias mentioned (Table 1).

Table 1:
The characteristics of papers decided at the end of the scoping review
Lead Author Journal Year Country Population (healthcare professions) Method(s) of simulation mentioned in article Bias types Description of bias
Adamson, K. [70] Nursing education perspectives 2016 USA Simulation participant-raters (nurse) Video-recorded simulations Implicit bias Race, ethnicity bias
Al-Moteri, M. [71] Australian Critical Care 2019 Australia Final-year undergraduate nurses, nurses enrolled in Masters or PhD programs. Screen-based simulated scenario Cognitive Bias Perceptual, attention, confirmation biases
Altabbaa, G. [72] Diagnosis 2019 Canada Medical students, post-graduate year (PGY) 1 IM residents, Simulated clinical environment Cognitive Bias Momentum, confirmation, playing-the-odds, order-effect biases
Arber, S. [17] Social Science & Medicine 2006 USA Primary care doctors Video vignette Implicit bias Gender, age, SES, race biases
Barnato, A. [55] Crit Care Med. 2011 USA Emergency physicians, hospitalists, and intensivists SPs Implicit bias Race bias
Barnato, A. [73] Med Decis Making 2014 USA EM physicians Video-encounters Implicit bias Race bias
Bennett, P. [74] Clin Teach 2016 Australia Medical, nursing, allied health students Immersive/wearable simulation Implicit bias Age bias
Berg, K. [18] Acad Med 2015 USA Medical students OSCEs Implicit bias Gender, race and ethnicity biases
Boada, L. [19] Comput Methods Programs Biomed 2018 Spain Undergraduate nursing students High fidelity simulators Implicit bias Gender bias
Bond, W. [75] Acad Med 2004 USA Emergency medicine residents High fidelity simulators Cognitive bias Decision-making
Boulet, J. [20] Adv Health Sci Educ Theory Pract 2005 USA Medical students (CSA candidates)/physician note raters SPs Implicit and Cognitive Biases Gender and rater bias
Braun, L. [76] Diagnosis 2019 Germany Medical students Electronic case simulation platform Cognitive Bias Premature closure bias
Brown, S. [77] Community Ment Health J 2010 USA Undergraduate medical students Simulation of auditory hallucinations Implicit bias Illness stigma
Brown, SA. [78] Community Ment Health J 2010 USA Undergraduate students Simulation of auditory hallucinations Implicit bias Mental illness stigma
Bucknall, T. [79] J Adv Nurs 2016 Australia Nursing students (final year) SPs Cognitive bias Premature closure and confirmation biases
Burgess, D. [80] Soc Sci Med 2008 USA Internal medicine physicians Video vignettes Implicit bias Race bias
Cavalcanti, R. [56] Acad Med 2014 Canada Residents in internal medicine OSCEs and High fidelity simulators Cognitive bias Not specified
Chen, A. [81] Am J Pharm Educ 2011 USA Pharmacy students Geriatric medication game and SPs Implicit bias Age bias
Choi, H. [82] Nurse Educ Today 2016 Korea Undergraduate nursing students SPs Implicit bias Mental illness stigma
Chugh, U. [83] Med Teach 1993 Canada Physicians/immigrant patients SPs Implicit bias Race and Ethnicity bias
Cicero, M. [84] Prehosp Emerg Care 2014 USA SPs, high-fidelity manikins, and low-fidelity manikins Disaster simulation scenarios using SPs, high-fidelity manikins, and low-fidelity manikins Cognitive bias Bias towards a specific pediatric disaster triage strategy
Claramita, M. [85] Nurse Educ Today 2016 Indonesia Nursing students OSCE with SPs Cognitive bias SES bias
Clark, C.M. [86] Nurse Educ 2019 USA Undergraduate nursing students Role play Implicit bias Uncivil behavior bias
Crapanzano, K. [87] J Gen Intern Med 2018 USA Internal medicine residents SPs Implicit bias Mental illness stigma
Dearing, K. [88] J Nurs Educ 2008 USA Nursing students Voice simulation mimics auditory hallucinations Implicit bias Mental illness stigma
Dedy, N. [45] Surgery 2015 Canada Surgery residents OSCE Implicit bias Rater bias
Denney, M. [21] Educ Prim Care 2016 UK GPs OSCE Implicit bias Ethnicity and gender bias
Doyle, K. [46] J Grad Med Educ 2014 Canada Faculties and residents of family medicine programs Simulated a tri-college, on-site ER for internal review (IR) process Implicit bias Rater/reviewer bias
Eisenberg, E. [22] J Gen Intern Med 2019 USA First-year residents in the Internal Med-Residency Program Simulation scenarios included interactions with SPs Implicit bias Race, ethnicity, nationality, religion, gender, sexual orientation, disability, physical appearance, SES biases
Eva, K. [89] Acad Med 2010 Canada Primary care physicians Videotaped vignette Cognitive bias Confirmation, and premature closure bias
Evans. J. [90] Issues Ment Health Nurs 2015 Australia 2nd year nursing students Simulated auditory hallucinations for schizophrenics Implicit bias Mental illness stigma
Feldman, H. [23] Health Serv Res 1997 USA Physicians Simulated scenarios on videotapes by professional actors Implicit bias Age, gender, race, and SES biases
Fitzgerald, S. [91] MedEdPORTAL 2018 USA Health professions students from multiple disciplines SPs Implicit bias Ethnicity bias
Fletcher, G. [47] Br J Anaesth 2003 UK Anesthetists SPs Cognitive bias Rater bias
Floyd, K. [92] J Physician Assist Educ 2015 USA Physician assistant students/2nd yr of MS degree and SPs SPs Cognitive bias Inflation bias
Foster, K. [93] Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2008 USA 1st year dental students Role play practice Cognitive bias Presentation bias
Galletly, C. [94] Aust N Z J Psychiatry 2011 Australia/New Zealand Medical students (final year) Video presentation/Simulated auditory hallucinations Implicit bias Mental illness stigma
Gispert, R. [24] Med Educ 1999 Spain Undergraduate medical students SPs Implicit bias Gender bias
Goddard, L. [95] J Neurosci Nurs 1998 USA Nursing students Role play Implicit bias Disability bias
Gostlow, H. [96] Ann Surg 2018 Australia Surgical trainees and consultant surgeons Video of operating theater sim – SPs Implicit bias Hierarchy bias
Gotlieb, R. [97] Gynecologic Oncology Reports 2019 Canada Medical doctors (staffs and residents) Software (computer) simulation scenarios. Cognitive Bias Gender effects on cognition
Greene, R.E. [25] Med Educ 2014 USA Medicine residents SPs Implicit bias Gender bias
Greene, R.E. [26] J Grad Med Educ 2017 USA Medicine students SPs Implicit bias Gender bias
Hahn, T. [98] J Man Manip Ther 2014 USA Physical therapists Video vignettes of SPs Cognitive bias Confirmation bias/ and training bias
Hales, C. [99] Ostomy Wound Manage 2018 USA Health care staff Immersive/wearable simulation Implicit bias Weight bias
Haliko, S. [100] Med Decis Making 2018 USA Physicians High fidelity simulator and SPs Cognitive bias Preference and comfort bias
Hanson, M. [101] Acad Psychiatry 2008 Canada Adolescent standardized patients SPs Implicit bias Mental illness stigma
Hareli, S. [102] Int J Psychol 2013 Israel Undergraduate medical students Video on computer screen simulation Implicit bias SES bias
Hermann-Werner, A. [103] BMJ Open 2019 Germany Medical students SPs and immersive/wearable simulation Cognitive Bias Weight bias
Hillbrand, M. [104] Psychiatr Rehabil 2008 USA Nurses, psychiatric technicians, psychologists and social workers, rehabilitation therapist. Role play Implicit bias Bias against prisoners
Hirsh, A. [27] J Pain 2010 USA Nurses Virtual human (VH) videos Implicit bias Gender, race, age bias
Hu, Y. [105] Adv Health Sci Educ Theory Pract 2015 USA Undergraduate medical students Simulation-based suturing task Cognitive bias Overestimation bias
Huber, M. [106] J Adv Nurs 1992 USA Healthcare care personnel Simulated handicaps Implicit bias Age bias
Hunter, J. [107] Nurse Educ Today 2018 UK Nursing Students Immersive/wearable simulation Implicit bias Weight bias
Jaeken, M. [108] Front Psychol 2017 Belgium Undergraduate psychology students Role play Cognitive bias Self-enhancement bias
Jaworsky, D. [109] AIDS Care 2017 Canada Medical Students SPs Implicit bias HIV stigma
Jensen, K. [48] Surg Endosc 2019 Multi-centered Medical students Virtual reality simulator Cognitive bias Self-enhancement and self-diminishment bias
Junnola, T. [110] J Clin Nurs 2002 Finland Nurses Screen-based computer simulated case Cognitive bias Confirmation bias
Kales, H. [28] Psychiatr Serv 2005 USA Psychiatrists Video vignettes of SPs Implicit bias Race and gender bias
Kennedy, D. [29] Nurse Educ Today 2020 Qatar Male nursing students Role-play, moderate and high fidelity simulators, SPs, simulated maternity clinic Implicit bias Gender bias
Khazadian-Figueroa, M. [111] J Nurs Staff Dev 1997 USA CNAs (certified nursing aids) Simulation game Implicit bias Age bias
Kidd, L. [112] Issues Ment Health Nurs 2015 USA Undergraduate psychiatric nursing students Hearing Distressing Voices Audio Simulation Implicit bias Mental illness stigma
Kim, M. [30] Comput Inform Nurs 2016 Korea Nursing students Simulation-based learning/hybrid SP and Noelle human simulator Implicit bias Gender bias
Kumagai, A. [31] Med Teach 2007 USA Faculty ‘Forum Theater’ techniques; simulated classroom discussion Implicit bias Race, gender, sexual orientation, SES bias
Kushner, R. [113] BMC Med Educ 2014 USA Medical students SPs Implicit bias Weight bias
LaRoche, K. [114] Contraception 2015 Canada Postabortion support team Unannounced standardized patient Implicit bias Abortion stigma
Levett-Jones. T. [115] Nurse Educ Pract 2011 Australia Nursing Students Videos w/SPs to train assessors Implicit and Cognitive biases Rater/Assessor bias
Lewis, C. [116] BMC Palliat Care 2016 UK Nursing and medical students High fidelity simlulator and SP Implicit bias Attitudes towards death
Li, L. [117] Int J Epidemiol 2014 China Hospital service providers Unannounced standardized patient Implicit bias HIV stigma
Lockeman, K.S. [118] Nurse Educ Today 2017 USA Nursing and medical students high fidelity – mannequins and SPs Implicit Bias Provider stereotypes
Lohman, P. [119] Percept Mot Skills 2008 USA Graduate students majoring in communications disorders Peer role play Implicit bias Attitudes towards stutterers
Lorenzo, A. [120] Fam Pract 2015 France Practicing providers SPs Cognitive bias Desirability bias/Hawthorne bias
Magpantay-Monroe, E. [121] Nurse Educ Today 2017 USA Nursing students SPs Implicit bias Military and veteran bias
Marceau, L. [32] J Eval Clin Pract 2011 USA Primary care physicians Video simulation Implicit bias Age, race, gender, SES biases
March, C. [122] J Grad Med Educ 2018 USA Pediatric residents SPs Implicit bias Age, race and ethnicity stigma
Margolis, M. [33] Acad Med 2002 USA Medical doctors Computer-based case simulation Implicit bias Gender and language bias
Maruca, A.T. [123] Nurse Educ Perspect 2018 USA Nursing students High fidelity (manikin) simulation Implicit bias Gender bias
Maupome, G. [124] Eur J Dent Educ 2002 Mexico Senior dental students SPs Implicit bias SES bias
McCave, E. [34] MedEdPORTAL 2019 USA Students’ of different health professions SPs Implicit bias Gender bias
McGrath, J. [49] West J Emerg Med 2015 USA EM residents High fidelity and virtual reality simulators Implicit bias Rater/observer bias
McNiel, P.L. [35] J Nurse Educ 2018 USA Nursing students Role play Implicit bias Gender biases
Minehart, R. [125] Anesthesiology 2014 USA Anesthesia faculty Role play/videos/SPs Cognitive bias Not specified
Mirza, A. [126] MedEdPORTAL 2018 USA Pediatric interns, upper-level residents (PGY-2 and PGY-3), and six fellows. SPs Cognitive Bias Premature closure bias
Mohan, D. [127] BMC Emerg Med 2016 USA Emergency physicians Virtual video games simulation Cognitive bias Poorly-calibrated heuristics
Nerup, N. [50] Gastrointest Endosc 2015 Denmark Physicians (10 experienced endoscopists and 11 trainees) High fidelity simulator Implicit bias Rater/observer bias
Nicolai, J. [36] Patient Educ Couns 2007 Germany General practitioners SPs Implicit bias Gender bias
Norman, R. [128] J Nurs Educ 2001 Australia RNs (nurses) Simulation game, peer role playing Implicit bias Bias against illicit drug users
O’Lynn, C. [129] J Nurse Educ 2014 USA Male nursing students Video and practice on manikins/debriefing Implicit bias Gender bias
Padilha, J. [57] J Med Internet Res 2019 Portugal Nursing students Virtual reality simulator Cognitive bias Bias in clinical reasoning
Paige, J. [130] J Surg Educ 2019 USA General surgery residents/ emergency medicine residents/senior undergraduate nursing students High fidelity simulation Implicit bias Hierarchy bias
Park, C. [58] Simul Healthc 2014 USA Residents, Anesthesiology (PG2) Simulated operating room/simulated scenario Cognitive bias Not specified
Patterson, F. [131] Med Educ 2018 UK Medical students High fidelity simulator Implicit bias Ethnicity bias
Pennaforte, T. [132] JMIR Res Protoc 2016 Canada General Pediatrics and Neonatal-Perinatal Medicine residents. Simulation scenario and standardized health professionals Cognitive bias Not specified
Persky, S. [133] Ann Behav Med 2011 USA Undergraduate medical students Immersive virtual environment/computer generated Implicit bias Weight bias
Prakash, S. [134] BMC Med Educ 2017 Australia Interns (medical students) High-fidelity simulator and SPs Cognitive bias Search satisfying, premature closure, and anchoring bias
Raemer, D.B. [37] Acad Med 2016 USA Anesthesiologist Simulated scenarios Implicit bias Hierarchy, gender and stereotypes bias
Richey Smith, C. [135] Am J Pharm Educ 2016 USA Pharmacy students Simulation game Implicit bias SES bias
Richmond, A. [136] MedEdPORTAL 2017 USA Students/medicine, nurse, pharmacy SPs Implicit bias Hierarchy bias
Ruparel, R. [137] J Surg Educ 2014 USA 27 urology residents Virtual reality simulator Cognitive Internal bias (experience w/simulator not translating to surgery affecting confidence)
Rutledge, C. [138] Contemp Nurse 2008 USA Nurses Computer generated virtual learning platform, high performance simulators (HPS). Implicit bias Cultural bias
Sargeant, S. [38] Adv Health Sci Educ Theory Pract 2017 Australia Medical students/SPs SPs Implicit bias Culture, age, gender biases
Schuler, S. [139] Stud Fam Plann 1985 USA/Nepal Family planning staff SPs Implicit bias Hierarchy bias
Sidi, A. [140] J Patient Saf 2017 USA Residents High fidelity simulator Cognitive bias Anchoring, availability bias, premature closer and confirmation bias
Siegelman, J.N. [39] J Grad Med Educ 2018 USA Emergency medicine residents Simulated cases – SPs, nurses, and simulation operators Implicit bias Gender bias
Silverman, A.M. [141] Disabil Rehabil 2018 USA Masters of occupational therapy (1st year) Impairment simulation (role play) Implicit bias Anti-disability and discriminatory bias
Stockmann, C. [40] J Nurse Educ 2017 USA Nursing students Manikin Implicit bias Gender bias
Svendsen, M. [51] World J Gastrointest Endosc 2014 Denmark Ten consultants experienced in endoscopy (gastroenterologists, n = 2; colorectal surgeons, n = 8) and eleven fellows Virtual reality simulator Implicit and Cognitive biases Rater/observer bias
Theodossiades, J. [142] Ophthalmic Physiol Opt 2012 UK Optometrists Unannounced standardized patients Cognitive Self-reporting bias
Thompson, C. [143] J Adv Nurs 2012 UK Nursing students, nurses Low and high fidelity/paper cases and human simulation (manikins not actors) Cognitive Judgment bias
Tollison, A.C. [41] J Nurse Educ 2018 USA Male nursing students Online simulation Implicit bias Gender bias
Underman, K. [42] MedEdPORTAL 2016 USA Undergraduate medical students SPs Implicit bias Gender bias
Varas-Diaz, N. [144] J Gay Lesbian Soc Serv 2019 USA Physicians in training SPs Implicit bias Gender and sexual orientation bias
Watson, M. C. [145] Pharm World Sci 2004 UK Emergency medicine residents Simulation lab scenario/high fidelity simulation Cognitive bias Selection bias
Welch, L. [43] J Health Sco Behav 2012 UK Primary care physicians Video vignettes of SPs Implicit bias Gender bias
Wijnen-Meijer, M. [52] Adv Health Sci Educ Theory Pract 2013 Netherlands/Germany Physicians SPs Cognitive bias Rater bias
Wiskin, C. [44] Med Educ 2004 UK Medical students Role-play Implicit bias Gender bias
Woda, A. [146] Nurs Educ Perspect 2019 USA Nursing students Simulated clinical environment Cognitive bias Bias in clinical reasoning
Worth-Dickstein, H. [147] Teach Learn Med 2005 USA Medical Students SPs Implicit bias SP scoring, personal, race, ethnic, and age bias
Wu, B. [148] BMC Med Educ 2016 Hong Kong Medical students Simulated cases – cognitive mapping Cognitive bias Bias in clinical reasoning
Yeates, P. [149] BMC Med 2017 UK Undergraduate medical students SPs Implicit bias and Cognitive bias Race, ethnicity, and examiner, recollection bias
Yu, C. [150] J Am Geriatr Soc 2012 Taiwan Nursing assistants SPs Implicit bias Age bias
Yuan, M. [151] Interact J Med Res 2013 USA Nurse evaluators SPs Cognitive bias Premature closure, anchoring, confirmation, and framing bias
Yudkowsky, R. [152] Acad Med 2015 USA Medical students SPs Cognitive bias Confirmation bias
Yule, S. [153] World J Surg 2008 Scotland Surgeons Videos of SPs and High fidelity simulator Cognitive bias Competency bias
Zottmann, J.M. [154] GMS J Med Educ 2018 Germany Medical students High fidelity simulator Cognitive bias Competency bias


The exploration of types of biases and dual theory

Dual process theory is a well-accepted framework for understanding decision-making processes and bias. This theory explains our thinking processes as either type 1 or type 2. Type 1 thinking is a fast, intuitive, pattern recognition-focused problem-solving method that creates a low cognitive burden on the user and enables quick decisions. Type 2 thinking is a slower, more methodical, thoughtful process. Therefore, an optimal balance of type 1 and type 2 processes is required to prevent biases for optimal clinical practice [15].

In dual process theory, type 2 thinking can bring a higher cognitive strain on the user but allows them to evaluate data more critically and look beyond patterns, and may potentially be more appropriate for complex problem solving. The current opinion among psychologists is that we spend approximately 95% of our time in type 1 thinking [16]. Cognitive bias (and the resulting errors) are more likely during the type 1 process [15].

Optimal diagnostic approaches are likely to use both type 1 and type 2 thinking at appropriate times. Non-analytical (type 1) reasoning has been shown to be just as effective as reflective reasoning to diagnose routine clinical cases. Furthermore, not all biases are caused by type 1 processing, but it is believed that when bias occurs, it can only be solved by activating type 2 processing. The articles we reviewed showed that the biases explored in articles on SBT were related to both cognitive and implicit biases, both of which can be associated with the two types of dual theory.

Bias types in simulation training

In this scoping review, we looked for all types of cognitive and implicit biases in SBT of health professions. Implicit biases were explored more than cognitive biases (Figure 5). The most researched implicit bias in health professions’ SBT is ‘gender bias’ [17–44]. Gender bias was also explored in different types of health professions and with different levels of experience: residents, primary care physicians, medical students, nursing students, etc. The most researched cognitive bias in literature is ‘decision-making (premature closure)’ [20,45–52]. We noted some biases that were not found in other reviews: uncivil behavior bias; poorly calibrated heuristics; and selection bias of patient participants [53–56]. In several papers, the type of bias was not specified and in those instances, we classified the biases based on the article’s content [20,47,51,52]. We were unable to further classify the types of bias explored in a couple of papers [57,58]. Our review indicates the prevalence of undefined bias in simulation training, which supports the importance of educators’ awareness of bias. All biases explored were classified under cognitive and implicit biases.

Types of biases.
Figure 4:

Types of biases.

Simulation training methods.
Figure 5:

Simulation training methods.

Cognitive bias

Cognitive bias is defined as unconscious and automatically developed mental processing strategies. These strategies are developed as adaptive mechanisms to simplify the complex inflow of information ultimately leading to biased judgments and inferences [59].

Cognitive bias and its impact are an important parameter on decision-making processes [60,61]. Cognitive bias, also known as ‘heuristics’, are cognitive shortcuts to help us make decisions [62]. It is increasingly accepted that significant diagnostic error can result from cognitive bias [63]. Clinical decision-makers have a risk of error due to biases that are not associated with intelligence or any other measure of cognitive ability [64]. In addition, individuals lack awareness of how these biases can affect their perceptions as they are unaware that their judgments are biased. The doctors who describe themselves as ‘excellent’ decision-makers and ‘free from bias’, often lack insight into their own bias [65].

We explored papers on the effects of different simulation methodologies on clinical reasoning and decision-making, and we explored which types of biases affect clinical reasoning and decision-making in SBT.

Implicit bias

The natural tendency of the mind is to rely on type 1 thinking, interpret data through heuristic scanning, and establish quick connections with data and experiences already available. Beyond cognitive bias, which affects clinicians’ interpretation of clinical data, there are intuitive screening and systematic biases on how we perceive other people, including patients. The ways we perceive and classify other individuals based on their characteristics (i.e. social and cultural biases) are most likely shaped by the experiences we have been exposed to. In clinicians, these biases appear in parallel with the general population [66]. Implicit bias (sometimes called unconscious bias) affects interpersonal interactions in ways that we are not consciously aware of. The health and behavioral effects of these implicit attitudes can be important. Implicit bias has many dimensions. Some examples of implicit biases are: race or ethnicity, gender, age, weight, sexual orientation, education and socioeconomic status [67].

Meanwhile, experimental studies have repeatedly shown that these biases measurably affect clinical assessments and treatment decision-making [68]. This effect seems particularly significant in challenging or ambiguous situations, or under heavier cognitive loads.

In addition, we noted that the number of articles published on the topic of bias in simulation in healthcare professional training increased dramatically from 1985 until 2020. This increase could reflect the increasing attention paid to decision-making processes and bias in general. It could also be a snowball effect – the more papers published on a topic, the more authors become inspired to explore new data on biases in SBT.

Biases exposed in different simulation training methods

Biases were explored using different simulation methods (Figure 4). Most of the articles exploring biases in simulation training involved SP methodology. This may reflect the importance of SP methodology as a training approach, its prevalence, or the particular need for well-designed scenarios in SP methodology. While SP methodology was the modality most often referred to in the articles, other modalities were also present (i.e. manikins, videos, etc.)

All trainings can be subject to bias. SBT has enhanced learning, however, trainers and learners can benefit from understanding that biases might be present in SBT [58,69]. The results also show that biases can be explored in any type of training methods in simulation, indicating that simulationsists should be aware of bias in training during all types of training.


One limitation of our review is that we only reviewed articles available in English. Additionally, there is no comprehensive classification guide for biases, especially implicit biases, so, we had some difficulties defining or naming some types of bias mentioned in the papers.

Another limitation is that we only reviewed articles found in one database, it is possible that some articles on bias in simulation training of healthcare professionals are included in a database other than PubMed. We also focused on peer-reviewed literature and therefore did not include literature produced outside of traditional academic publishing.


Understanding how bias affects SBT for healthcare professionals is important, as it affects not only how future providers are educated and develop their clinical decision-making skills, but also because of its impact on patient care and health outcomes. This review not only showed the depth of the types of bias examined in the literature, but also found some biases that had not been previously classified.

In future, researchers might explore how biases affect clinical reasoning and decision-making in SBT. Researchers might also explore how to avoid bias in simulation by looking at instructional design of SBT.

There are many opportunities for researchers to explore bias and its impact on SBT. Once SBT trainers become aware of the possible presence of bias in their methodology, they may adjust existing instructional design, better follow established best practices and create new best practices to help identify and address these biases.



We acknowledge and thank Maureen Clark, Librarian at University of Illinois Chicago for her assistance in developing the search terms for this scoping review.

Authors’ contributions

Selçuk Akturan, MD, Christine Park, MD, and Amy Binns-Calvey all made substantial contributions to the conception or design of the work as well as acquisition and analysis of the data. All authors contributed to and provided final approval of the manuscript to be published and are accountable for the accuracy and integrity of the manuscript.


None to declare.

Availability of data and materials

Availability of data and materials: The datasets analyzed during the current review are available from the corresponding author on reasonable request.

Conflict of interest

This research does not contain any human subjects.

Ethics approval and consent to participate

The authors declare that they have no conflict of interest.


None declared.



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