A Scoping Review on the Potential Impact of Implicit Bias in Healthcare Settings for Children with Acquired Brain Injuries

Abstract

Purpose/Objective.

Acquired brain injury (ABI) is a leading cause of disability in children and adolescents. Implicit biases within pediatric ABI healthcare settings may exert negative effects on clinical interactions and medical decision-making processes. This study aimed to conduct a scoping review of current research that examines the potential impact of implicit biases in such healthcare settings for children with ABI.

Research Methods/Design.

Following PRISMA-ScR guidelines, a search among five databases (CINAHL, Cochrane CENTRAL, PubMed, PsycARTICLES, and PsycINFO) was conducted, followed by abstract/full-text screening and data extraction. Main characteristics of the included studies, including research design, relevance to implicit biases, and public health impact were synthesized. The risk of bias of included studies was assessed.

Results.

Out of the 203 articles returned from the literature search, a total of three studies met the inclusion criteria for the present review. All studies examined the relevance of racial/ethnic biases for evaluating abusive head trauma (AHT) or nonaccidental trauma (NAT). The included studies had an overall unclear to low risk of biases.

Conclusions/Implications.

This scoping review identified scarce but emerging evidence of the presence and detrimental impact of potential implicit biases on the access and quality of care received by children with ABI. More research is needed to examine the causes, process mechanisms, and consequences of implicit biases in pediatric ABI healthcare settings.

Implicit bias, defined as bias outside of conscious processing that influences behavior (Greenwald & Banaji, 1995), has been found to be present in most Americans especially toward “othered” or marginalized groups (Xu et al., 2014). Unlike explicit biases – a consciously held belief or cognition – implicit biases are motivated by unconscious stimuli or beliefs below the level of conscious cognition (Brownstein & Zalta, 2019). While explicit biases can be directly studied through self-report, implicit biases are often more elusive and require specialized tests like the Implicit Association Test (IAT, (Greenwald et al., 1998)) or the Implicit Relational Assessment Procedure (IRAP, (Power et al., 2009)). These tests have been used in the exigent literature to better understand biases that might be susceptible to a social desirability effect like opinions on racial/ethnic (Whitford & Emerson, 2019), LGBTQ+ (Lelutiu-Weinberger et al., 2022), or socioeconomic (Fung et al., 2023) minority group members.

Despite the elusive nature of implicit bias, recent research on implicit bias within healthcare settings has identified key areas where implicit bias could be most likely to exert negative effects on the access and quality of healthcare services provided to the general patient population (Chapman et al., 2013; Groves et al., 2021; Hall et al., 2015). For instance, research has suggested that implicit bias was associated with an increased risk of misdiagnosis and differential treatment recommendations for medical care outcomes in patients at risk of stroke (Bhat et al., 2021), patients from racial/ethnic minorities (Dovidio et al., 2016), child abuse victims (Rojas et al., 2017), and patients with disabilities (Byrne & Tanesini, 2015; Chisolm-Straker & Straker, 2017; FitzGerald & Hurst, 2017; VanPuymbrouck et al., 2020).

Specifically for pediatric patients, recent research focused on health outcomes for pediatric diabetes (Addala et al., 2021; Odugbesan et al., 2022), pediatric mental health (Liu et al., 2022), emergency department decision making (Dehon et al., 2017), pediatric cancer patients (Beltrami et al., 2022), pediatric racial/ethnic disparities in general (Guzek et al., 2022b; Maina et al., 2018) and within the COVID-19 context specifically (Mulchan et al., 2021) have all examined the potential negative impact of healthcare professional implicit bias for their respective settings. While study methodology varies by specific healthcare setting, generally these studies have attempted to understand or address these implicit biases by increasing awareness of implicit bias through debiasing learning seminars (Griffith et al., 2021) or tailored medication treatment recommendations across demographic characteristics (Baker et al., 2010; Baker et al., 2015; Sahin et al., 2019).

As a subgroup of general pediatric patients, children with acquired brain injuries (ABIs) – brain damages caused by events after birth (Teasell et al., 2007) – have been known to be at a greater risk of both mortality and disability with over 2,000 pediatric mortality cases related to ABI each year (Faul et al., 2010; Iqbal O’Meara et al., 2020). Given that children with ABI have increased complications in medical care compared to the general population (Fu et al., 2015), it is imperative to understand how implicit biases in healthcare settings caring for pediatric ABI patients may negatively impact the access and quality of healthcare services and patient outcomes in this vulnerable population.

Such an understanding is crucial as evidence of racial/ethnic healthcare disparities among children with ABI accumulates. For example, population-based research has revealed race/ethnicity as a significant predictor associated with both the presence of post-injury medical care-seeking behavior and total medical expenditures where pediatric patients with TBI who were Black or African American were found less likely to receive medical treatment and incur less medical expenditures after a TBI (Shen et al., 2021). Furthermore, analyses of national hospital administrative data dedicated to TBI patients (e.g., the Traumatic Brain Injury Model System database) suggested that these racial inequalities persist in brain injury patients’ long-term rehabilitation outcomes, reflected in continuing disparities of longitudinal trajectories of life satisfaction among adolescents and young adults with TBI (where patients who identified themselves as Black were more likely to be in the low-stable, high-decreasing, or low-increasing trajectories than the high-stable trajectory) (Shen & Wang, 2022).

Beyond race/ethnicity, the TBI literature has also identified additional risks of the negative impact of potential provider implicit biases on healthcare disparities in the general brain injury patient population. For example, one recent study used group-based trajectory modeling to investigate disparities in perceived mental health trajectories among 949 adults with TBI using 12 years of data from the 2004–2015 Medical Expenditure Panel Survey (Feldman et al., 2020). The study found that factors related to families’ socioeconomic statuses such as insurance status and family poverty levels, which are susceptible to provider implicit biases, were significant predictors of perceived mental health outcomes of these adult patients with TBI (e.g., families with private insurance and high income were found more likely to have better perceived mental health outcomes) (Feldman et al., 2020).

However, to the best of authors’ knowledge, no systematic synthesis of existing literature to gain an in-depth understanding of the potential impact of implicit biases in pediatric (or adult) ABI healthcare settings is available in the literature. This study aims to address this significant gap by conducting a scoping review of existing empirical evidence related to implicit bias in healthcare settings that care for children with ABI.

Methods

Ethics Review

The present study was not reviewed by the Institutional Review Board (IRB) because the research activities are not human subjects research as defined by 45 CFR 46.

Search Strategy

Following PRISMA-ScR guidelines (Tricco et al., 2018), the original literature search was conducted on February 23^rd, 2022 with a supplementary search on February 18^th, 2023 to update the original search results. For both searches, the following five health science databases were searched: CINAHL, Cochrane Central Register of Controlled Trials (CENTRAL), PubMed, PsycARTICLES, and PsycINFO. The search was not constrained by publication year or language. The search terms used for this review were: (implicit OR unconscious OR subconscious) AND (bias* OR IAT OR attitud* OR prejudice OR racism OR discrimin*) AND (brain injur* OR TBI OR ABI OR stroke OR brain hemorrhage OR brain hypoxia OR encephalitis). These terms were selected based on the definition of implicit bias and other reviews on this topic (FitzGerald & Hurst, 2017). Grey literature was also incorporated into the search. Supplementary searches were conducted by checking the reference list of relevant articles. All search results were transferred to Covidence, a web-based collaboration software platform for conducting literature reviews (Veritas Health Innovation, 2020)(Covidence, 2022), which automatically detects duplicates and has them removed before finalizing the database for abstract screening. The search protocol was not pre-registered.

Inclusion and Exclusion

Inclusion criteria included: (1) Sample population must be children and adolescents less than 18 years of age with an acquired brain injury (ABI), defined as brain damage caused by events after birth, including but not limited to neurosurgery, stroke, brain tumors, physical trauma, infection, poisoning, hypoxia, ischemia, or substance abuse. A study with both children and adult participants would be eligible if the child data could be extracted independently from the adult data. (2) Studies must have reported at least one outcome that was discussed by authors as conceptually related to implicit provider biases toward pediatric patients with ABI. Examples of an eligible outcome may include (but are not limited to) direct assessment of implicit biases or differential medical decisions (e.g., diagnosis, treatment plans) that may be related to implicit biases. (3) All research designs were considered.

Exclusion criteria include: (1) non-empirical articles, such as commentary and review papers, (2) studies published in a language other than English and no suitable translator was available. However, the reference lists of relevant review articles were examined to identify potential studies missed in the initial search.

Findings of studies that were ineligible to be included in this scoping review but still relevant to the review topic in general were incorporated into the discussion section to provide contextualization of the review findings.

Screening

After removing the duplicates, two independent reviewers assessed the title and abstract for all remaining article citations, followed by screening the full-text articles of those that passed through the title/abstract screening phase. For both title/abstract and full-text screening, the inclusion and exclusion criterion described in the previous section were followed and disagreements were solved through discussion. If consensus was not achieved, a third (senior) reviewer’s feedback was solicited. Consistent with the literature guidelines in reporting agreement indexes, the present study examined the inter-rater agreement using both the proportionate agreement and kappa metrics, which are conceptually similar to sensitivity and specificity for diagnostic testing measures (Cicchetti & Feinstein, 1990; Feinstein & Cicchetti, 1990). Accordingly, the resulting inter-rater agreement was 0.90 for Proportionate Agreement, 0.71 for Cohen’s Kappa during the title and abstract screening phase, and 0.98 for Proportionate Agreement and 0.79 for Cohen’s Kappa during the full-text review stage. All screening took place on the Covidence platform.

Plan for Data Synthesis

Following PRISMA-ScR guidelines (Tricco et al., 2018), relevant data from the included studies were independently extracted by the two independent reviewers using a pre-schemed data extraction template created by the study team. The extracted data included the author, study country, publication year, sample characteristics, intervention description, comparison group description, study design, and primary outcomes. Characteristics of the included studies were qualitatively synthesized to create descriptive summaries of the study design and main findings of the included studies.

Transparency and Openness

The authors have reported how the scoping review searched and screened for eligible studies to be included in the review, all procedures related to data inclusion/exclusion, data extraction, and data analysis. The protocol of this review was not pre-registered. However, materials for this study, including search protocol and search/screening records are available upon reasonable requests made to the corresponding author.

Results

Literature Search and Screening

Figure 1 presents the PRISMA flowchart diagram for the results from the literature search and screening processes. As hypothesized, a limited number of studies investigating healthcare professionals’ bias and impact on ABI patients were found. A total of 219 articles were found including 203 articles from the main literature search plus 16 additional articles retrieved from reference lists of relevant review articles. A total of 41 duplicates were then identified and removed, which resulted in 178 articles for the title and abstract screening. A total of 40 articles entered the full-text screening phase. Unfortunately, the Covidence software did not allow us to document the reasons for exclusion at the title/abstract screening phase (unlike for the full-text screening), so we are unable to describe the statistical distribution of the number of articles being excluded at this stage per each exclusion reason. But readers are assured that each article’s title and abstract were reviewed against the established inclusion/exclusion criteria and an article was only excluded when two independent reviewers both voted to exclude it. By the conclusion of the full-text screening, a total of 37 articles were excluded, resulting in three studies (Hymel et al., 2018; Jenny et al., 1999; Rangel et al., 2009) meeting the eligibility criteria of this scoping review.

Figure 1.

Flow Chart of Study Identification and Selection Process

Main Characteristics of Included Studies

All three studies took place in the United States, and only one (Rangel et al., 2009) had an interventional design. All articles focused on patients who were evaluated for abusive head trauma (AHT) or nonaccidental trauma (NAT) and for whom racial/ethnic data was examined. Ages ranged from birth to three years of age. The main characteristics of the three studies, including sample characteristics, study design/methodology, main study findings, and relevance to implicit bias/disparities, are detailed in Table 1.

Table 1.

Descriptive Characteristics of Included Studies (N=3)

Author(s), year	Sample N, M age in years, range in years, % female	Study Design	Intervention Description	Methodology and procedure	Outcomes	Relevance to Implicit Bias
Jenny et al., 1999	173, ~7 months, 10 days to 2.9 years, 45%	Retrospective chart review	Not Applicable	Charts of identified patients were reviewed according to predetermined criteria to evaluate ‘missed’ vs. recognized AHT. Characteristics for two sets of patient groups were extracted and compared on age, race, family composition, and severity of symptoms.	Factors associated with unrecognized AHT were age (younger than six months), race (white), parents living together, and symptoms noted at the first visit (children without respiratory comprise or seizures).	White Children less than 6 months old from intact families were found to be most at risk for underdiagnosis of AHT. Implications of decision-making influences.
Rangel et al., 2009	Pre-intervention group: 208, 3 months, 0.1–12, 41.3% Post-intervention group: 52, 3 months, 0.1–12, 42.3	Retrospective chart review	Screening guidelines (flow chart for physician) were created to streamline head injury evaluation process.	Hospital database was searched between 2003–2008 for patients under 12 months with head injuries. Patients screened and evaluated before implementation of guidelines in 2006 were compared with patients screened and evaluated after guidelines.	Before guidelines, Black patients underwent more skeletal surveillance for NAT compared to White patients. In addition, White patients with private insurance were screened more often than white patients without private insurance. After guidelines implemented, no statistically significant differences were found for patients across both racial groups for screening rates of NAT	Policy-based intervention utilized to decrease disparity in the evaluation of head injury for infants. Implications of post-guideline procedure statistics suggest implicit racial and income biases contributed to provider recommendations.
Hymel et al., 2018	500, less than three years old, age range not reported, female percentage not reported Patients were recruited from 18 sites	Secondary analysis of patients from PediBIRN database	Not Applicable	Period of 3 years (2010–2013). Comparison of evaluation of AHT to frequency of reporting between White and minority race/ethnicity patients. Site-specific analysis also conducted to correct and account for site-specific discrepancies in reporting/evaluation that could confound the design.	Minority patients were more frequently evaluated and reported for AHT, but only in patients who were categorized as low risk for AHT. Only 2 out of 18 sites had more extreme disparities.	Deprived of obvious confounds, the 2 sites with extreme disparities reported could potentially be due to physician implicit racial bias. Findings suggest that multi-site analysis could potentially obscure site-level factors that contribute to treatment disparities such as provider implicit bias.

Research Design

Jenny et al. (1999) utilized a retrospective chart review design. Rangel et al. (2009) used a prospective interventional design. Hymel et al. (2018) used aggregate and site-specific analysis of a secondary cross-sectional dataset, PediBIRN. Hymel et al. (2018) had the largest sample size and included eighteen participating sites. Grounded on earlier studies signifying minority children are more likely to be accused of NAT (Flaherty et al., 2002), Rangel et al. (2009) developed a screening guide for performing skeletal surveys in all infants with an unwitnessed head injury, then used a pre- and post-policy-based intervention in evaluating NAT in minority and disadvantaged children.

Disparities or Bias-Related Findings

All three studies found racial and ethnic disparities in provider-patient interactions possibly influencing the evaluation of pediatric ABI. For example, Hymel et al. (2018) compared AHT evaluation and reporting frequencies among white/non-Hispanic and minority race/ethnicity populations. The authors reported that out of 500 acutely head-injured children in intensive care, patients of minority race/ethnicity (n=229) were more commonly evaluated (p < .001) and reported (p = .001) for suspected physical abuse. The disparities occurred almost exclusively in lower-risk patients, which were categorized as non-AHT (according to a prior definition provided in Table 1 of Hymel et al. (2018)) based on previous publications (Hymel et al., 2014; Hymel et al., 2013), or if their patient-specific probability of AHT was less than 25% (Hymel et al., 2018).

Furthermore, Jenny et al. (1999) found that AHT is more likely unrecognized in young White children. Results illustrated that in 37.4% of White children, AHT cases were missed, compared to 11% in minority children. Interestingly, family composition differences also resulted in the identification of potential abuse. For example, those with two parents had a higher possibility of having unrecognized AHT than children with a missing mother or father (p = .003).

Pre-intervention, Rangel et al. (2009) found African Americans underwent skeletal surveillance more than Whites (n=208; 90.5% compared to 69.3%; p = .01), whereas after the utilization of the screening guidelines, both African Americans and Whites were evaluated similarly (n=52; 92.3% compared to 84.6%, p = 1.0). In addition to racial and ethnic inequalities, insurance and economic disparity were also discussed in the Rangel et al. (2009) study. Whites were less likely to be screened prior to the guidelines, and Whites with private insurance were less prone to be screened than counterparts with no insurance or with government insurance (50% compared to 88.1%, p < .001). Conversely, insurance type did not influence screening in the African American population.

Potential Health Impact

The findings of the included studies of this review suggest that while implicit biases may be unknown to the individual, there is the possibility that medical decision-making might nonetheless be altered by these unconscious beliefs (Hymel et al., 2018). Such implicit biases might have a critical impact on pediatric ABI patients’ health and healthcare outcomes such as misdiagnoses. For example, Jenny et al. (1999) found 54 cases (31.2%) to be classified as missed concerning unrecognized abusive head trauma. When authors compared missed cases to recognized cases, their differences by age, race, family composition, and severity of symptoms at the initial visit were all statistically significant. Regrettably, the misclassification of abuse allowed those children to be reinjured because of the delay (Hymel et al., 2018; Jenny et al., 1999).

Risk of Bias Assessment

The methodological quality of the included studies was assessed in terms of sample representation, outcome data, confounding, and selective reporting by two raters using a risk-of-bias template developed by the study team, guided by the Cochrane guidelines (Lundh & Gøtzsche, 2008) and the Risk of Bias in Non-randomized Studies of Exposure (ROBINS-E) (Higgins et al., 2022). Overall, the included studies had an unclear to low risk of bias. Table 2 describes the summary of the risk of bias assessment.

Table 2.

Risk of Bias Assessment

Author(s), year	Bias	Classification	Support for Classification
Jenny et al., 1999	Selection	High Risk	Risk related to one recruitment location with no additional stratification conducted to certify the participants represented a larger population.
	Attrition	Low Risk	Related to age and incomplete medical records.
	Confounding	Unclear	Steps were utilized to address potential confounds when comparing across groups, but more information is needed based on complicated design examining risk of abuse through medical records.
	Reporting	Unclear	Outcome measures related to study hypothesis appear to be reported. Unknown if additional analysis not reported were conducted; more information needed.
Rangel et al., 2009	Selection	Low Risk	Niche population, specific to institution. Not enough information to consider high risk of bias
	Attrition	Low Risk	Excluded participants for reasons not relevant to the outcome criteria.
	Confounding	High risk	Limitations on ethnic/racial composition of database. Acknowledged by authors, but not controlled for in methodology.
	Reporting	Unclear	Outcome measures related to study hypothesis appear to be reported. Unknown if additional analysis not reported were conducted; more information needed.
Hymel et al., 2018	Selection	Low Risk	Sampling done at multiple urban centers; possible risk of bias given the lack of stratification for the sample on generalized population.
	Attrition	Unclear	No information on incomplete outcome data; retroactive secondary analysis.
	Confounding	Low Risk	Controlled for confounding variables in differences between compared populations.
	Reporting	Unclear	Outcome measures related to study hypothesis appear to be reported. Unknown if additional analysis not reported were conducted; more information needed.

Discussion

The present scoping review aimed to examine empirical evidence in the existing literature regarding the potential influence of implicit biases within pediatric ABI healthcare settings by conducting a comprehensive search in multiple worldwide academic databases that focus on healthcare research. Our search identified a total of three studies that met the review inclusion criteria, which included a mixture of retrospective and prospective interventional research designs. All three studies found racial and ethnic disparities in provider-patient interactions among pediatric patients with ABI under three years old and suggested that implicit biases may serve as at least one possible influencing factor that may unknowingly impact individual providers’ decision-making outcomes. Methodological quality of the included studies was moderate with unclear to low risk of bias.

The present study identified scarce but emerging data that suggested the possible existence of potential provider implicit biases that may have exerted unwanted effects on pediatric patients with ABI who are under three years old, especially for those who are from racial/ethnic minority backgrounds. This finding is consistent with the existing literature on examining racial/ethnic healthcare disparities among pediatric patients with brain injuries as reviewed earlier, including medical-seeking behavior and expenditures (Shen et al., 2021) and longitudinal rehabilitation outcomes such as life satisfaction (Shen & Wang, 2022). The findings are also consistent with prior systematic and narrative reviews that documented the existence of possible implicit biases among healthcare providers that may be associated with health disparities in populations beyond pediatric ABI patients (Beltrami et al., 2022; Dehon et al., 2017; FitzGerald & Hurst, 2017; Mulchan et al., 2021). However, it should be recognized that the etiology of disparities in healthcare can be multifactorial (Khan et al., 2017; Manuck, 2017; Warren Andersen et al., 2022). Therefore, despite the documented healthcare disparities and the suspected role of provider implicit biases towards patients from stigmatized groups in nonverbal communication and provider communication style (FitzGerald & Hurst, 2017; Schnierle et al., 2019), more research is still needed to provide converging evidence that directly links provider implicit biases with (1) negative clinical interactions, (2) worsened clinical decisions (Zestcott et al., 2016), and (3) worsened patient outcomes (Schnierle et al., 2019).

In fact, as suggested by the present review, there is still limited research that directly assesses implicit biases in healthcare settings for pediatric ABI patients in the first place. Note that articles identified by the present review mostly demonstrated the impact of disadvantaged sociodemographic and economic factors such as race/ethnicity, household income, and insurance status on provider-patient interactions and/or clinical decisions. But whether such an impact was reflective of only providers’ implicit bias towards patients from these disadvantaged groups was unclear. Encouragingly, such direct assessment of implicit biases of healthcare professionals using implicit association tests (IATs) has been increasingly reported in the pediatric literature other than ABI, such as implicit attitudes among (1) pediatricians for patients with asthma, attention deficit hyperactivity disorder (ADHD), pain, and urinary tract infections (UTIs) (Sabin & Greenwald, 2012), (2) emergency department providers for the child or the caregiver of American Indian children in (Puumala et al., 2016), and (3) pediatric orthopedic surgeons (Guzek et al., 2022a). The findings of this research using IATs had confirmed the presence of provider implicit biases toward racial/ethnic minority groups in these non-ABI pediatric patients.

The burgeoning research using IATs in non-ABI pediatric populations highlights an important gap in the pediatric ABI literature because only when reliable and accurate measurement of implicit biases in pediatric ABI healthcare becomes available can researchers and practitioners alike gain a more in-depth understanding of the presence and scope of implicit biases within healthcare settings. The availability of these IAT-based research findings can then lead to (1) examination of the relationship between implicit biases, clinical interactions (e.g., verbal and nonverbal communication styles), medical decisions (e.g., treatment plan, medical diagnosis, prescriptions), and ultimately health outcomes such as physical and cognitive rehabilitation outcomes following TBI or quality of life (Schnierle et al., 2019); and (2) effective evaluation of implicit bias mitigation programs using various debiasing strategies with high fidelity (Arif & Schlotfeldt, 2021; Hagiwara et al., 2020; Stone & Moskowitz, 2011).

Study Limitations and Constraints on Generality

First, although the present review identified three studies that touched on the impact of implicit biases within healthcare settings on pediatric patients with ABI, the findings should be interpreted with caution as none of these three studies were dedicated to examining the association between implicit biases and disparities in provider decisions/patient outcomes. There may be understudied alternative factors other than provider implicit biases that also exert a significant impact on medical decisions and rehabilitation outcomes of pediatric patients with ABI. Second, due to the lack of sufficient research data on this topic, only qualitative synthesis and not quantitative synthesis (e.g., meta-analysis) was conducted in the present review. Third, as suggested by the risk of bias assessment results, limited information was available to determine the quality of some of the evidence synthesized in the present study. Furthermore, the potential presence of publication bias should be noted. There may be studies conducted on implicit biases and/or racial/ethnic disparities in pediatric ABI healthcare that remain unpublished and therefore not included in the search results of the present review. Finally, the small number of eligible studies included in the present review should caution readers when interpreting the conclusions drawn from the present review. Note that all three identified studies focused on infants/toddlers younger than 3 years old who were diagnosed/suspected of AHT/NAT. Accordingly, it appears that there is a significant dearth of published studies regarding implicit biases in healthcare settings for pediatric patients with ABI who are older or have brain injuries caused by mechanisms beyond AHT/NAT. As an increasingly important but still emerging field of research, more systematic efforts are required by scientists, practitioners, healthcare administrators, and policymakers to collaboratively assess and mitigate (e.g., through educational/training programs) the negative impact of potential implicit biases on broader populations of pediatric patients with ABI.

Conclusion

This scoping review synthesized current literature on the examination of the potential impact of implicit biases within healthcare settings for pediatric patients with ABI. Data analyzed in this review found scarce but emerging evidence of the possible presence and resulting detrimental impact of implicit biases (at least as one of the influencing factors) on the access and quality of care received by children with ABI. More research is needed to systematically and directly assess the underlying mechanisms of implicit biases. Future research should aim to develop and implement theory-driven, evidence-based, and accessible interventional programs for mitigating implicit biases within all levels of pediatric ABI healthcare settings.

Impact.

• Acquired brain injuries (ABIs) pose a significant threat to the health of U.S. children and adolescents as one of the leading causes of childhood disability in the country. The present scoping review aims to serve as the first step to creating a bias-free healthcare environment for pediatric patients with ABI and their families in the future.

• This study served as the first comprehensive search and review of existing empirical evidence examining the potential impact of implicit biases within healthcare settings for children with ABI on clinical interactions (e.g., verbal and nonverbal communication styles), medical decision-making (e.g., treatment plan), and patient health outcomes (e.g., post-TBI rehabilitation outcomes).

• Insights gained from this scoping review lay the knowledge foundation for future scientists, health care professionals, medical administrators, and policymakers to develop effective bias mitigation programs to promote the inclusiveness and equity of healthcare services and reduce healthcare disparities for children with ABI.