Factors underlying differences in knowledge,explicit stigma and implicit biases towards autism across Hong Kong,the United Kingdom and the United States

Abstract

A growing literature suggests that there is cross-cultural variation in levels of autism-related stigma, which may partially be explained by differences in cultural orientation or autism-related knowledge between countries. This literature has relied heavily on self-report measures of explicit attitudes towards autism; little research has examined implicit biases, and whether these implicit biases vary across cultures. Thus, here we had two aims: (1) to assess the contribution of knowledge and cultural orientation to both explicit stigma and implicit biases, and (2) to compare autism-related knowledge, explicit stigma, and implicit biases across university students in Hong Kong (n = 119), the United Kingdom (n = 120), and the United States (n = 122). Replicating prior work, we found that explicit stigma was predicted by less accurate knowledge, lower horizontal collectivism, greater vertical individualism, and higher implicit biases. Implicit biases were directly predicted by age and explicit stigma, and indirectly predicted by vertical individualism (positively) and horizontal collectivism (negatively) via explicit stigma. Knowledge and explicit stigma differed across countries, even after accounting for covariates: students in Hong Kong displayed less accurate knowledge, and higher explicit stigma towards autism, than those in the United Kingdom and United States. However, implicit biases did not differ between countries.

Lay abstract

Attitudes towards autism vary across countries. Some of this variation could reflect differences in cultural values across countries, or differences in how much people know about autism. Until now, most research on this topic has asked people directly about their attitudes towards interacting with autistic people. As a result, we understand little about why some people unconsciously hold negative attitudes towards autism, and whether these unconscious attitudes vary across countries. We studied explicit attitudes (willingness to interact), implicit attitudes (unconscious beliefs), knowledge about autism, and cultural values in university students from Hong Kong, the United Kingdom and the United States. We found that people were less willing to interact with autistic people if they knew less about autism, aligned with a competitive and hierarchical society (‘vertical individualism’), did not see themselves as part of a collective whose members are equal (less ‘horizontal collectivism’), and if they unconcsciously associated autism with negative attributes. Students in Hong Kong were less willing to interact with autistic people and had less understanding of autism compared to those in the United Kingdom and the United States. Unconscious biases did not differ across countries. Our findings highlight the need to combat misconceptions about autism to improve attitudes towards autistic people, especially in Hong Kong. Unfortunately, our results suggest that acquiring more accurate knowledge may not be sufficient to alter unconscious biases. Further research is needed to determine the factors underlying unconscious biases.

Keywords

autism spectrum disorders collectivism cross-cultural culture environmental factors explicit attitudes implicit biases individualism knowledge stigma

Introduction

Autistic people¹ face stigma in their everyday lives (see Turnock et al., 2022). Neurotypicals often form negative first impressions of autistic individuals (Sasson et al., 2017), are less likely to want to spend time with an autistic person as friends (Nevill & White, 2011), and are less likely to want an intimate relationship with an autistic person (Jensen et al., 2016). Indeed, autistic individuals can be dehumanized by their neurotypical peers (Cage et al., 2018). Unfortunately, these attitudes can have a negative impact, resulting in exclusion, bullying, self-stigma (see Han et al., 2022), and poorer mental and physical health for autistic people (e.g. Botha & Frost, 2020; Cage et al., 2018; Keating et al., 2024; Turnock et al., 2022) and their families (see Papadopoulos et al., 2019). Increased awareness of these negative consequences has resulted in a growing number of studies aiming to identify the factors underlying autism-related stigma.

Factors underlying autism-related stigma

Historically, the majority of global autism research has taken place in Western countries, such as the United States and United Kingdom (Rong et al., 2022; Sweileh et al., 2016). While cultural variation exists within and beyond national borders (Henderson et al., 2013), confining research to a single region/population reduces cultural variability and can lead researchers to take cultural norms for granted, whereas between-group comparisons can illuminate relevant cultural differences (Oyserman, 2017). Thus, cross-cultural comparisons could be useful for identifying the factors underpinning stigma (Keating et al., 2021, 2024).

An emerging body of research suggests that autism-related stigma – measured as self-reported, explicit behavioural intentions towards autistic people – is higher in South Korea (Kim et al., 2022), Japan (Keating et al., 2024; Someki et al., 2018), Lebanon (Gillespie-Lynch et al., 2019; Obeid et al., 2015), and Malaysia (de Vries et al., 2020) than in the United States or United Kingdom. This suggests an opportunity to identify relevant factors that could, in principle, be driving these differences in stigma, such as varying levels of knowledge about autism, or differences in cultural value orientation between these countries (see Gillespie-Lynch et al., 2019; Keating et al., 2024).

One factor that has been found to contribute to autism-related stigma is a lack of, or less accurate knowledge about autism (Gillespie-Lynch et al., 2019; Kim et al., 2022; Kitchin & Karlin, 2021; Kuzminski et al., 2019; Ling et al., 2010; Obeid et al., 2015). Although public awareness of autism may have improved in parts of the world such as Australia (S. C. Jones et al., 2021), Denmark (Jensen et al., 2016), France (Durand-Zaleski et al., 2012), South Korea (Park et al., 2018), the United Kingdom (Cage et al., 2019; Keating et al., 2024), and Canada (G. E. Mitchell & Locke, 2015), even in these countries there is evidence of superficial and outdated knowledge, and persisting misconceptions about autism and autistic people (Jensen et al., 2016; S. C. Jones et al., 2021; Park et al., 2018; White et al., 2019). As argued by Turnock et al. (2022), this ‘limited knowledge . . . has a critical role in the presence of stigmatized views, as an individual is more likely to rely on reductionist labels and stereotypes, dichotomize into them versus us, and ultimately behave in a discriminatory way’ (p. 77). As well as this limited knowledge contributing to higher autism-related stigma, it is important to note that knowledge rooted in certain ideologies may precipitate stigma. For example, knowledge based on the medical model – which frames autism as a pathology – is linked to higher levels of autism-related stigma (Gillespie-Lynch et al., 2017), while support for the neurodiversity movement is associated with reduced stigma (Kim & Gillespie-Lynch, 2023). Finally, research has also found that increasing autism knowledge through training can lead to some reductions in stigma among university students (Gillespie-Lynch et al., 2015; Jones, DeBrabander, & Sasson, 2021a; Jones, Morrison, et al., 2021; Obeid et al., 2015; Saade et al., 2021), especially when training programmes are developed by, or in collaboration with autistic people (Gillespie-Lynch et al., 2022). Together, this work provides convincing evidence that autism-related stigma may, at least in part, be underpinned by incomplete or inaccurate knowledge.

Another factor that could underpin differences in levels of autism-related stigma is cultural value orientation. One conceptualization of cultural value orientation decomposes it into two independent, orthorgonal dimensions: individualism-collectivism and verticality-horizontality. While individualistic cultures place emphasis on independence and distinctiveness, collectivistic cultures emphasize community interdependence and shared group norms (Triandis, 1988). Relatedly, while horizontal cultures value equality, vertical cultures value hierarchy, among its members (Shavitt et al., 2006; Singelis et al., 1995; Triandis & Gelfand, 1998). A growing body of evidence suggests that autism-related stigma is higher in collectivistic than in individualistic cultures (e.g. in Japan, Lebanon, South Korea than in the United Kingdom or the United States; Gillespie-Lynch et al., 2019; Kim et al., 2022; Obeid et al., 2015; Someki et al., 2018), and that higher verticality is linked to greater stigma towards autistic and neurodivergent people (Gillespie-Lynch et al., 2019; Rao et al., 2010).

Notably, however, studies that have broken down the individualism-collectivism and verticality-horizontality dimensions into their constituent elements (vertical individualism, horizontal individualism, vertical collectivism, horizontal collectivism; Triandis & Gelfand, 1998) have found that heightened vertical individualism (seeing oneself as autonomous within a justly hierarchical system) is linked to elevated self-reported stigma towards autistic and neurodivergent people (Gillespie-Lynch et al., 2019; Kim et al., 2022; Rao et al., 2010). Horizontal collectivism (seeing oneself as part of a collective whose members are equal), on the other hand, was linked to reduced stigma (Gillespie-Lynch et al., 2019; Kim et al., 2022).

Thus, a growing body of work suggests that there is cross-cultural variation in levels of autism-related stigma, which may be explained (at least in part) by differences in knowledge or differences in cultural orientation between countries. A limitation of this previous work, however, is that it has relied heavily on self-report measures of stigma, investigating explicit attitudes towards autism (e.g. through Social Distance Scales (SDS); Gillespie-Lynch et al., 2019; Kim et al., 2022; Obeid et al., 2015; Someki et al., 2018). Such explicit biases are heavily influenced and controlled by social desirability (Dovidio et al., 1997; Henderson et al., 2012; Stier & Hinshaw, 2007), potentially obscuring underlying or unconscious biases, which persist even among raters who report no explicit biases (see Dickter et al., 2020; Hinshaw & Stier, 2008). To the best of our knowledge, prior research has compared levels of both explicit and implicit attitudes towards autism across only two countries (namely, South Korea and the United States; Kim et al., 2023). This study found that both forms of stigma were higher in South Korea than in the United States and elucidated several factors that may predict implicit biases. For example, Kim and colleagues (2023) identified that, while older age and reduced social dominance orientation predicted higher implicit biases in South Korea, the absence of a (nuclear) autistic family member predicted higher implicit biases in the United States. At present, further research is necessary to investigate cross-cultural differences in, and the factors underpinning explicit and implicit biases towards autism across additional countries and cultures. Such investigations may shed light on how to minimize both explicit and more deeply ingrained implicit biases, and facilitate the identification of priority regions and needs for anti-stigma interventions.

The current study

In this study, we first aimed to determine the contribution of knowledge and cultural orientation (among other factors) to both explicit stigma and implicit biases towards autism, and second, to compare levels of knowledge, explicit stigma, and implicit biases across Hong Kong, the United Kingdom, and the United States, after controlling for relevant covariates. We selected these countries for three key reasons: (1) their varied cultural orientations enable us to examine whether different values contribute to explicit stigma, implicit biases, and knowledge (by providing sufficient statistical variability); (2) autism-related stigma and knowledge remain understudied in Hong Kong; and (3) although the United Kingdom and the United States are often used as comparison groups in research (e.g. de Vries et al., 2020; Gillespie-Lynch et al., 2019; Kim et al., 2022; Obeid et al., 2015; Someki et al., 2018), no studies have directly compared these factors between them. To fulfil our aims, university students from Hong Kong, the United Kingdom and the United States completed a number of questionnaires and an autism-related Implicit Association Test (IAT; Jones, DeBrabander, & Sasson, 2021a). In line with previous findings (e.g. Gillespie-Lynch et al., 2019; Keating et al., 2024; Kim et al., 2022; Obeid et al., 2015; Someki et al., 2018), we first predicted that those with greater knowledge about autism, higher horizontal collectivism, and lower vertical individualism would exhibit lower levels of explicit autism-related stigma. Second, we hypothesized that levels of explicit stigma and implicit biases would be higher in Hong Kong than in the United Kingdom and United States for several reasons: (1) previous studies have shown widespread autism-related stigma in Hong Kong, with about 75% of caregivers reporting such experiences (Ng & Ng, 2022); (2) acceptance of inequality is higher in Hong Kong (verticality score = 68, compared to 35 in the United Kingdom and 40 in the United States; Hofstede, 1984, 2001), which is linked to increased stigma towards autistic and neurodivergent people (e.g. Gillespie-Lynch et al., 2019; Rao et al., 2010); and (3) Hong Kong’s strong focus on social conformity, neoliberal values, and deficit-based accounts of autism (Cheng et al., 2023; Grinker, 2020; Thomas, 2021), and limited exposure to the neurodiversity movement (Cheng et al., 2023; Kim & Gillespie-Lynch, 2023; Turnock et al., 2022) may further amplify stigma.

Method

This study was approved by the Science, Technology, Engineering and Mathematics ethics committee at the University of Birmingham (ERN_21-1732), and the Human Research Ethics Committee from the University of Hong Kong (EA210523). The study was conducted in accordance with the principles of the revised Helsinki declaration.

Participants

In total, 368 university students took part in our study (N = 125 Hong Kong, N = 120 United Kingdom, N = 123 United States). These participants were recruited by advertising the study via University of Hong Kong mailing lists (Hong Kong) and through Prolific (the United Kingdom, the United States) between May 2022 and July 2022. Seven participants were excluded as their IAT data did not meet criteria for inclusion (further details below; Greenwald et al., 2003). As such, our final sample comprised 361 university students; 119 from Hong Kong, 120 from the United Kingdom, and 122 from the United States. All participants were fluent in English, eliminating the need to translate the study materials into Chinese. This allowed us to maintain consistency in the meaning of the questionnaire items and IAT terms, thus ensuring that any differences in stigma, implicit biases, or knowledge arose due to true group differences rather than subtle language discrepancies. Information on participants’ age, gender, course level, course type, and student type across each of these countries can be seen in Table 1. Participants’ ethnicities are reported in Supplementary Materials A.

Table 1.

Participant demographics. For age, the mean is presented with the standard deviation in parentheses. For gender, course level, course type, and student type, the number of participants within each group is presented.

Characteristic	Hong Kong (n = 119)	United Kingdom (n = 120)	United States (n = 122)
Age	22.26 (4.36)	28.74 (10.93)	26.02 (8.21)
Gender	75 Cisgender female, 38 Cisgender male, 1 Non-binary/third gender 5 Prefer not to say	79 Cisgender female, 37 Cisgender male, 2 Non-binary/third gender, 1 Other, 1 Prefer not to say	67 Cisgender female, 48 Cisgender male, 3 Non-binary/third gender, 2 Transgender female, 2 Other
Course Level	88 Undergraduate, 31 Postgraduate	78 Undergraduate, 41 Postgraduate	89 Undergraduate, 33 Postgraduate
Course Type	107 Full-time, 12 Part-time	91 Full-time, 29 Part-time	91 Full-time, 31 Part-time

Materials and procedure

After providing informed consent, participants completed an online survey using the Qualtrics survey platform (Qualtrics, 2005). The survey began with a series of demographic questions, including participant age, gender, ethnicity, country of birth, country of residence, course level (e.g. undergraduate, postgraduate), course discipline, and student type (e.g. local, international, exchange). Following this, participants completed three previously validated questionnaires: the SDS (Gillespie-Lynch et al., 2019), the Participatory Autism Knowledge Measure (PAK-M; Kim et al., 2022), and the Culture Orientation Scale (Triandis & Gelfand, 1998). Finally, participants completed an autism-related IAT (Jones, DeBrabander, & Sasson, 2021a) on Gorilla.sc (an online experiment platform). All participants completed the study in English.

Explicit stigma

Here, we employed a version of the SDS (originally, Bogardus, 1933), adapted by Gillespie-Lynch et al. (2015) for the context of autism. In this 11-item self-report questionnaire, participants rate their willingness to engage with an autistic individual across various levels of intimacy (e.g. ‘I would be willing to spend an evening socializing with an autistic person’) on a 5-point Likert-type scale from ‘Strongly disagree’ to ‘Strongly agree’. Stigma scores on this scale range from −22 to 22 with higher scores indicating higher stigma. Notably, neutral or negative scores should not be interpreted as an absence of stigma (see comments on a similar measure by S. C. Jones (2022)). The SDS boasts strong internal consistency in previous work (α = 0.88–0.89; Gillespie-Lynch et al., 2019), and in this study (α = 0.92, Hong Kong α = 0.87, United Kingdom α = 0.92, United States α = 0.92).

Knowledge about autism

The PAK-M is a self-report questionnaire assessing knowledge about autism (Kim et al., 2022). The PAK-M was adapted from the Autism Awareness Survey (Stone, 1987) and was developed with autistic input to reflect changing knowledge and research (Kim et al., 2022). In this questionnaire, participants are required to respond to 29 true-or-false statements (e.g. ‘Autism can be diagnosed as early as 18 months’, ‘Autistic children grow up to be autistic adults’) on a 5-point scale ranging from 1 = ‘Definitely False’ to 5 = ‘Definitely True’. Higher scores represent more accurate and current knowledge about autism. Previous works, and our analyses here, demonstrate that the PAK-M has high internal consistency (Kim et al., 2022: α = 0.71; here: α = 0.80, Hong Kong α = 0.72, United Kingdom α = 0.76, United States α = 0.85).

Cultural orientation

The Culture Orientation Scale is a 16-item questionnaire that assesses four components of cultural orientation: vertical individualism (e.g. ‘When another person does better than I do, I get tense and aroused’), vertical collectivism (e.g. ‘It is important to me that I respect the decisions made by my groups’), horizontal individualism (e.g. ‘I often do my own thing’), and horizontal collectivism (e.g. ‘If a coworker gets a prize, I would feel proud’; Triandis & Gelfand, 1998). To complete this questionnaire, participants are required to respond on a 9-point scale ranging from 1 = ‘Never or definitely no’ to 9 = ‘Always or definitely yes’. A score for each of the four components is calculated by summing the respones for the relevant items. To facilitate interpretability of one of our plots, we also computed an ‘acceptance of inequality variable’ by subtracting each individual’s total horizontal score from their total vertical score (as in Papadopoulos and colleagues, 2019). The Culture Orientation Scale has been shown to have acceptable internal consistency in previous work (e.g. Gillespie-Lynch et al., 2019: α = 0.75–0.79), and here (α = 0.72, Hong Kong α = 0.74, United Kingdom α = 0.73, United States α = 0.70).

Implicit biases

IATs (Greenwald et al., 1998) are used to probe automatic associations between cognitive concepts (e.g. Black, White) and positive or negative attributes (e.g. good, bad). In this study, we developed an IAT based on that from Jones, DeBrabander, and Sasson (2021a) to examine whether participants across our sites unconsciously associate autism diagnostic labels with pleasant or unpleasant personal attributes. The target words (see Table 2) used for each category were taken from Jones, DeBrabander, and Sasson (2021a).

Table 2.

Implicit Association Test categories and words.

Category	Stimulus words
Diagnosis
Autism spectrum	Autistic, Autism, Asperger’s
Typically developing	Normal, Neurotypical, No diagnosis
Personality traits
Pleasant	Nice, friendly, likeable, safe, popular, honest, compassionate, independent
Unpleasant	Awkward, creepy, weird, dangerous, antisocial, needy, unpredictable, helpless

Following the procedures of Jones, DeBrabander, and Sasson (2021a), our IAT task used a five-block design (see Table 3.). In the first block, participants categorized words (e.g. ‘Neurotypical’, ‘Autistic’) based on diagnostic concepts (‘Typically Developing’ and ‘Autism Spectrum’) presented on the left and right sides of the screen. Participants pressed the ‘e’ key to categorize a term as ‘Typically Developing’ or the ‘i’ key to categorize it as ‘Autism Spectrum’. The second block focused on personal attributes, with participants categorizing words (e.g. ‘Friendly’, ‘Awkward’) as either ‘Pleasant’ or ‘Unpleasant’. In the third block, the concept and attribute categories were displayed simultaneously in a prejudice consistent manner (e.g. ‘Typically Developing or Pleasant’ on the left of the screen, ‘Autism Spectrum or Unpleasant’ on the right of the screen), and participants categorized both concept and attribute words one at a time as they appeared. The fourth block was identical to the first block, except the positioning of the two concepts was reversed, with participants pressing the ‘e’ key to categorize a word as ‘Autism Spectrum’ and the ‘i’ key to categorize a word as ‘Typically Developing’. In the fifth block, the diagnostic concept and attribute terms were displayed simultaneously in a prejudice inconsistent manner (e.g. ‘Autism Spectrum or Pleasant’ on the left, ‘Typically Developing or Unpleasant’ on the right), and again participants categorized stimulus words into their respective categories.

Table 3.

Block order to autism Implicit Association Test.

Block order	Type of judgement	Left side	Right side
1	Diagnostic discrimination	Typically developing	Autism spectrum
2	Personality trait discrimination	Pleasant	Unpleasant
3	Prejudice consistent combination	Typically developing or pleasant	Autism spectrum or unpleasant
4	Diagnostic discrimination, reveresed order	Autism spectrum	Typically developing
5	Prejudice inconsistent combination	Autism spectrum or pleasant	Typically developing or unpleasant

Within blocks 1, 2, and 4 (the learning phases), each word was displayed three times in the centre of the screen in a random order, with each word appearing once before any repeats occurred. For blocks 3 and 5 (testing blocks), each word was displayed once in the centre of the screen in a random order over 22 trials. The inter-trial interval was 100 ms. Scoring for the IAT is based on the D-score, representing the standardized difference in response times for prejudice inconsistent and prejudice consistent pairings (Greenwald et al., 2003); positive scores indicate a relationship between a concept and negative attributes. The IAT showed high internal consistency across all five blocks in this study (overall α ⩾ 0.74; Hong Kong mean α = 0.72, UK mean α = 0.84, US mean α = 0.86), and in previous work (α ⩾ 0.87; Jones, DeBrabander, & Sasson, 2021a).

Raw response latencies were obtained for the IAT and inspected to determine whether any trials or participants exceeded common guidelines for inclusion (e.g. remove trials where response latencies >10,000 ms or <400 ms, remove participants that have latencies <300 ms on more than 10% of trials, etc.; Greenwald et al., 2003). This resulted in the removal of seven participants. Following this, D-scores were calculated using the revised scoring method outlined in Greenwald et al. (2003) and Jones, DeBrabander, and Sasson (2021a): participants’ mean response latencies for the prejudice consistent block were subtracted from those of the prejudice inconsistent block. These difference scores were then divided by the standard deviation of participants’ (own) response latencies across the two blocks, thus providing a standardized score that accounts for individual differences in response latencies. Scores significantly higher than zero indicate a significant association between autism and negative attributes; scores significantly lower than zero indicate a significant association between autism and positive attributes.

Community involvement

This project comprises a collaboration between autistic and non-autistic researchers. Both autistic and non-autistic researchers were involved in the conceptualization and design of the study, data collection and analysis, and in writing up the manuscript. Moreover, following participatory research guidelines (Fletcher-Watson et al., 2019; Keating, 2021), the researchers also consulted autism community members in their local country (in Hong Kong, the United Kingdom, and the United States, respectively) during the design-phase of the project.

Transparency and openness

All data files, data analysis scripts, and experimental tasks are openly available at https://osf.io/7cd6g/?view_only=f9e6d0e3375a44fa9f2e5a3767b58523. Our primary analyses were conducted in R Studio (version 2023.06.2) and our exploratory mediation analyses were conducted in JASP (version 0.17.2.1). Notably, we found significant cross-cultural variation in autism-related knowledge and explicit stigma both when we controlled relevant covariates (see Results) and when we did not, thus affording us confidence in our findings.

Statistical analyses

In the following section, we first determined whether levels of explicit stigma and implicit biases towards autism significantly differed from zero in the aggregated sample, using Wilcoxon signed-rank tests (with the wilcox.test() function). Second, we identified the factors contributing to explicit stigma, implicit biases, and knowledge within this aggregated sample using multiple regression (with the lm() function) and non-parametric multiple regression (with the gam() function). Examining these relationships in the aggregated sample allowed us to capture a wide range of variation in cultural orientation and autism-related knowledge, thereby increasing our ability to detect significant relationships (Keating et al., 2021). By identifying the significant predictors of our dependent variables, we could control for these factors in our cross-country comparisons, ensuring any observed differences were not confounded by variations in these predictors across countries (e.g. different cultural orientations, different levels of knowledge, different ages or genders). Finally, we compared levels of explicit stigma, implicit biases, and knowledge across Hong Kong, the United Kingdom and the United States, after controlling for relevant covariates using analyses of covariance (ANCOVAs, with the aov() function). For all analyses, we used a p < 0.05 significant threshold to determine whether to accept or reject the null hypothesis.

Results

Explicit stigma

A median explicit stigma score of −12 was found across the three countries. A Wilcoxon signed-rank test (using the wilcox.test() function) demonstrated that this value was significantly lower than zero (the neutral point on the stigma scale; V = 648.5, p < 0.0001). Notably, this score should not be interpreted as evidence of positive attitudes as it suggests that participants, on average, still expressed some discomfort around engaging with autistic people on at least one of the questions (see also S. C. Jones, 2022).

Implicit biases

A median IAT D-score of 0.75 was found across the three groups. A Wilcoxon signed-rank test (using the wilcox.test() function) demonstrated that this value was significantly higher than zero (V = 62,940, p < 0.0001), indicating that participants responded more quickly when autism labels were paired with unpleasant attributes compared to when these attributes were paired with non-autistic labels. As such, participants’ implicit attitudes towards autism were clearly negative (replicating Dickter et al., 2020).

Determining which factors contributed to explicit stigma, implicit biases, and knowledge

First, we aimed to determine which factors contributed to explicit stigma. Despite attempts to transform the data, parametric assumptions could not be met. Thus, we conducted a non-parametric multiple regression (using the gam() function) with age, gender, course level, autism-related knowledge, vertical individualism, horizontal individualism, vertical collectivism, horizontal collectivism, and implicit biases as predictors. We included age, gender, and course level as covariates here because previous research has found a contribution of these variables to autism-related stigma (Gillespie-Lynch et al., 2015; Kim, 2020; Kim et al., 2023; Kuzminski et al., 2019). Our analysis revealed that autism knowledge (t(345) = −10.46, p < 0.0001) and horizontal collectivism (t(345) = −2.03, p = 0.0428) were negative predictors, and vertical individualism (t(345) = 6.35, p < 0.0001) and implicit biases (t(345) = 2.17, p = 0.0306) were positive predictors, of explicit stigma. Those with less accurate autism-related knowledge, lower horizontal collectivism, higher vertical individualism, and higher implicit biases displayed greater levels of explicit stigma. There were no other significant predictors of explicit stigma (p > 0.05).

Next, we aimed to determine which factors contributed to implicit biases. After transforming the data (using the natural logarithm function), we conducted a multiple linear regression (using the lm() function) with age, gender, course level, autism-related knowledge, vertical individualism, horizontal individualism, vertical collectivism, horizontal collectivism, and explicit stigma as predictors. This analysis revealed that explicit stigma (t(345) = 2.17, p = 0.0306) and age (t(345) = 2.27, p = 0.0237) were positive predictors of implicit biases: those older in age and those who reported higher levels of explicit stigma, exhibited greater implicit biases. There were no other predictors (all p > 0.05).

Finally, to establish which factors contributed to autism knowledge, we conducted a multiple linear regression (using the lm() function, as parametric assumptions were met) with age, gender, course level, vertical individualism, horizontal individualism, vertical collectivism, and horizontal collectivism as predictors. This revealed that vertical individualism (t(347) = −3.05, p = .0025) and vertical collectivism (t(347) = −2.39, p = 0.0175) were negative predictors, and horizontal individualism (t(347) = 3.45, p = 0.0006) and horizontal collectivism (t(347) = 4.38, p < 0.0001) were positive predictors, of autism-related knowledge: individuals with greater acceptance of inequality (i.e. higher verticality and reduced horizontality) displayed less accurate autism-related knowledge. Our analyses also identified a significant effect of gender (F(5, 347) = 2.65, p = 0.0228). Post hoc t tests revealed that non-binary individuals (mean (standard error of the mean; SEM)) = 128.67 (2.12)) had significantly higher levels of autism-related knowledge than those who preferred not to disclose their gender (mean (SEM) = 107.83 (5.68); t(10) = 3.20, p_bonf = 0.0225). There were no other significant predictors of knowledge (all p > 0.05).

Comparing attitudes towards, and knowledge about, autism across countries after controlling for relevant factors

Next, we aimed to compare levels of explicit stigma, implicit biases, and autism-related knowledge across countries, after controlling for variables that were significant predictors of these factors. We controlled for these significant predictors for three main reasons: (1) to ensure that any differences in explicit stigma, implicit biases, and/or knowledge between countries were not confounded by variation in the predictors across countries (e.g. different cultural orientations, different levels of knowledge); (2) to reduce omitted variable bias – in which the effects of omitted variables are mistakenly attributed to the predictors included in the model (Wilms et al., 2021; Wooldridge, 2012); and (3) to improve the precision of the estimated coefficients by accounting for additional sources of variation, leading to more reliable statistical inferences (Deaton & Cartwright, 2018; Hernández et al., 2004).

Explicit stigma

To determine whether there were differences in levels of explicit stigma between countries after controlling for knowledge, horizontal collectivism, vertical individualism and implicit biases (which were found to be significant predictors), we completed an ANCOVA (using the aov() function) on ranked data. This revealed a main effect of country (F(2, 354) = 22.12, p < 0.0001), which persisted even after accounting for the covariates. Post hoc Bonferroni-corrected Wilcoxon rank sum tests (using the wilcox.test() function) demonstrated that there were significantly higher levels of explicit stigma in Hong Kong than in the United Kingdom (W = 11,656, p_bonf < 0.0001) and the United States (W = 11,519, p_bonf < 0.0001), but no differences between these latter countries (W = 6676, p_bonf = 0.703; see Figure 1, top). In our ANCOVA, autism-related knowledge (F(1, 354) = −98.24, p < 0.0001) and horizontal collectivism (F(1, 354) = −4.28, p = 0.0392) were negative predictors, and vertical individualism (F(1, 354) = 33.00, p < 0.0001) and implicit biases (F(1, 354) = 5.45, p = 0.0201) were positive predictors, of explicit stigma (after accounting for country). In sum, those with lower levels of autism knowledge, lower horizontal collectivism, higher vertical individualism, higher implicit biases, and those in Hong Kong (relative to the United Kingdom and the United States) displayed greater levels of explicit stigma (see Figure 1).

Figure 1.

Plots displaying (1) levels of explicit stigma across Hong Kong, the United Kingdom, and the United States (top); (2) the relationships between explicit stigma and autism knowledge (middle-left), vertical individualism (middle-right), horizontal collectivism (bottom-left), and implicit biases (bottom-right) respectively, across Hong Kong, the United Kingdom, and the United States.

After inspecting Figure 1 (bottom-right), we noted that the strength of the relationship between explicit stigma and implicit biases appeared to differ slightly across countries. As such, we added an implicit biases × country interaction to our previous ANCOVA. This interaction was not significant (p = 0.419). Thus, there were no significant differences in the strength of the relationship between explicit stigma and implicit biases across countries.

Implicit biases

Next, to determine whether there were differences in levels of implicit biases between countries after controlling for age and explicit stigma (which were found to be significant predictors), we completed an ANCOVA (using the aov() function). In this analysis, there was no main effect of country (F(2, 354) = 0.86, p = 0.422). Only explicit stigma (F(1, 354) = 4.57, p = 0.0332) and age (F(1, 354) = 9.42, p = 0.0023) were significant predictors of implicit biases (see Figure 2).

Figure 2.

Scatterplots displaying the relationship between implicit biases and age (left), and implicit biases and explicit stigma (right) across Hong Kong, the United Kingdom, and the United States.

By inspecting the scatterplot depicting the relationship between implicit biases and age (Figure 2, left), it became apparent that this relationship may be moderated by country. Therefore, we added an age × country interaction to our previous ANCOVA. This interaction was significant (F(2, 352) = 3.57, p = 0.0293): while implicit biases increased with growing age in the United Kingdom (R = 0.20, p = 0.0305) and the United States (R = 0.26, p = 0.0047), there was no relationship between implicit biases and age in Hong Kong (R = −0.14, p = 0.1178).

Knowledge

Finally, to determine whether there were differences in levels of autism-related knowledge between countries after controlling for gender, vertical individualism, vertical collectivism, horizontal individualism, and horizontal collectivism (which were found to be significant predictors), we completed an ANCOVA (using the aov() function). This analysis revealed a significant main effect of country (F(2, 349) = 16.64, p < 0.0001), that persisted even after controlling for the covariates. Post hoc Bonferroni-corrected independent samples t tests (using the t.test() function) demonstrated that university students in Hong Kong displayed significantly lower levels of knowledge than in the United Kingdom (t(237) = −7.16, p_bonf < 0.0001) and the United States (t(239) = −6.09, p_bonf < 0.0001; see Figure 3, left). There were no significant differences in levels of knowledge between the United Kingdom and the United States (t(240) = 0.40, p_bonf = 1.000). In our ANCOVA, we found that vertical collectivism (F(1, 349) = −11.22, p = 0.0009) was a negative predictor, and horizontal collectivism (F(1, 349) = 18.09, p < 0.0001) and horizontal individualism (F(1, 349) = 5.87, p = 0.0159) were positive predictors, of autism-related knowledge. In sum, those with greater acceptance for inequality (i.e. higher verticality and lower horizontality), and those from Hong Kong (relative to the United Kingdom and the United States) displayed less accurate knowledge about autism.

Figure 3.

Plots displaying (1) levels of autism-related knowledge across Hong Kong, the United Kingdom and the United States (left), and (2) relationships between autism knowledge and acceptance of inequality across Hong Kong, the United Kingdom, and the United States.

Exploratory mediation analyses

Finally, since we had identified that knowledge, vertical individualism and horizontal collectivism contributed to explicit stigma, which in turn contributed to implicit biases, we conducted three post hoc, exploratory mediation analyses to test for indirect effects. This approach was important to determine whether explicit stigma acts as a mediating pathway through which culture and knowledge contribute to implicit biases, offering deeper insight into the mechanistic links between these variables. Here, we used SEM for our mediation analyses, rather than standard regression models (Baron & Kenny, 1986), as SEM is regarded ‘a more appropriate inference framework for mediation analyses’ (Gunzler et al., 2013, p. 391). We employed bias-corrected bootstrapping (with 5000 replications) to create confidence intervals (CIs) as there is a consensus that this is the most powerful method for testing mediated effects (Cheung, 2007; Fritz & MacKinnon, 2007; Hayes & Scharkow, 2013; MacKinnon et al., 2004; Valente et al., 2016). If these CIs do not cross zero, there is evidence for the experimental hypothesis; if these CIs cross zero, there is evidence for the null hypothesis (Lane et al., 2013). In our three models, the predictors were knowledge, vertical individualism, and horizontal collectivism, respectively, the mediator was explicit stigma, and the outcome variable was implicit biases. These analyses revealed significant indirect effects of vertical individualism (z = 2.44, 95% CI = 0.0021, 0.0183) and horizontal collectivism (z = −1.94, 95% CI = −0.0141, −0.0005) on implicit biases, via explicit stigma (vertical individualism: z = 8.05, 95% CI = 0.0476, 0.0810; horizontal collectivism: z = −5.08, 95% CI = −0.0724, −0.0330; see Figure 4): those higher in vertical individualism, and those lower in horizontal collectivism, exhibited elevated levels of explicit stigma, which in turn led to higher levels of implicit biases. Conversely, there was not a significant indirect effect of knowledge on implicit biases (z = −1.46, 95% CI = −0.0116, 0.0015). One interpretation of these results is that our culture shapes our explicit attitudes and behaviours by providing a set of ‘social rules’ to follow. For example, horizontal collectivistic cultures encourage people to report equitable attitudes towards others. Over time, repeatedly engaging with these positive explicit attitudes could alter underlying implicit attitudes, resulting in the (implicit) belief that all members are equal (see Discussion for a full interpretation).

Figure 4.

Mediation models showing the contribution of individualism (top) and acceptance of inequality (bottom) to implicit biases via explicit stigma.

Discussion

In this study, we first aimed to determine the contribution of knowledge and cultural orientation (among other factors) to both explicit stigma and implicit biases, and second, to compare levels of knowledge, explicit stigma, and implicit biases across Hong Kong, the United Kingdom, and the United States, after controlling for relevant covariates. Regarding our former aim, we found that higher explicit stigma was predicted by less accurate knowledge, greater vertical individualism, lower horizontal collectivism, and higher implicit biases. As such, our results add to a growing body of literature suggesting reduced knowledge may (at least in part) underlie heightened autism-related stigma (e.g. Gillespie-Lynch et al., 2015, 2019, 2022; Jones, DeBrabander, & Sasson, 2021a; Jones, Morrison, et al., 2021b; Kim et al., 2022; Kitchin & Karlin, 2022; Kuzminski et al., 2019; Ling et al., 2010; Obeid et al., 2015; Saade et al., 2021). Mechanistically, this inaccurate knowledge may result in individuals relying on reductionist steoretypes, dichotomizing into ‘them versus us’, and behaving in a discriminatory manner (Turnock et al., 2022). Beyond this, here we replicate previous findings suggesting that explicit stigma is predicted by greater vertical individualism (i.e. seeing the self as autonomous within a justly hierarchical system) and lower horizontal collectivism (i.e. not seeing the self as part of a collective whose members should be equal; Gillespie-Lynch et al., 2019; Kim et al., 2022), across all three countries studied.

Moreover, here, we found an association between explicit stigma and implicit biases, suggesting a degree of concordance between self-reported explicit attitudes, and underlying implicit attitudes, about autism. Visually, this association appeared to be strongest in the United Kingdom, but we observed no interaction with country. This finding contributes to the mixed literature on this topic: while some previous studies have found relationships between explicit and implicit measures of autism-related stigma (e.g. Kim et al., 2023, combined sample), others have not (Dickter et al., 2020; Jones, DeBrabander, & Sasson, 2021a). However, research outside of the autism literature suggests that IAT-based and self-report measures of attitudes towards groups are often associated (e.g. Hofmann et al., 2005).

Relatedly, we identified other direct and indirect relationships with implicit biases. With respect to the former, we found a (direct) positive association between implicit biases and age in the United Kingdom and the United States (but not Hong Kong): older adults typically displayed higher levels of implicit biases than younger adults. This finding is consistent with the results obtained by Kim et al. (2023) among South Koreans and it aligns with previous research documenting more pronounced biases regarding age (Chopik & Giasson, 2017), race (Gonsalkorale et al., 2009; Stewart et al., 2009), and disability (Goreczny et al., 2011), among older adults compared to younger ones.

Beyond this, our findings provide a novel contribution to the literature by elucidating factors that exert indirect effects on implicit biases via explicit stigma: vertical individualism and horizontal collectivism. An important theoretical question concerns how this may occur. From a mechanistic standpoint, culture may influence our explicit attitudes and behaviours by providing a set of ‘social rules’ to follow. For example, being immersed in a horizontal collectivistic culture – wherein the self is part of a collective whose members are seen as equal – encourages individuals to report equitable (explicit) attitudes towards others in order to preserve their social standing (or to avoid backlash from other members). Downstream, repeatedly engaging with these positive attitudes may extinguish and/or change the more durable, underlying beliefs (i.e. implicit biases) that have been reinforced over time. Thus, our cultural orientation may provide a set of ‘social rules’ that shape our explicit attitudes, which in turn influence our implicit attitudes. Although this is a logical possibility, given that SEM cannot definitively determine causality and directionality (Bollen & Pearl, 2013), further research employing longitudinal methods and causal manipulation is necessary (e.g. by testing how cultural orientation, explicit and implicit attitudes change after people move residence to a different culture) to confirm the nature of these relationships.

In this study, we also found that reduced autism-related knowledge was predicted by greater acceptance of inequality (i.e. higher verticality and lower horizontality), thus replicating previous results (Gillespie-Lynch et al., 2019). There are many potential explanations for this finding. On one hand, it could be that individuals who are more accepting of inequality are less inclined to seek out information or engage with marginalized groups with less power, resulting in poorer autism-related knowledge. On the other hand, the reverse direction of causality is possible: it may be that reduced or inaccurate knowledge about marginalized conditions and groups (e.g. autism and autistic people) predisposes individuals to be more accepting of inequality, as individuals rely on stereotypes and misconceptions which may paint these groups in a negative light. Alternatively, there could be a cyclical relationship, wherein reduced knowledge about marginalized groups (based on stereotypes and misconceptions) predisposes individuals to be more accepting of inequality, which in turn reduces their motivation to seek out information or engage with marginalized groups (thus maintaining incomplete or inaccurate knowledge), and so on. On a broader scale, it could be that fewer autism awareness campaigns – and/or autism awareness campaigns that spread less accurate or negative information about autism – are conducted within cultures with vertical orientations where unequal power distributions are more generally accepted or unquestioned by the public, organizations, and government, leading to reduced or inaccurate autism-related knowledge. Further work is necessary to ascertain the mechanisms underpinning the link between greater acceptance of inequality and limited autism-related knowledge.

Regarding our second aim, we identified significant cross-cultural variation in levels of autism-related knowledge and explicit stigma. Specifically, we found that there were lower levels of knowledge about, and greater (self-reported) explicit stigma towards, autism in Hong Kong than in the United Kingdom and the United States. These findings complement and extend previous results documenting greater knowledge, and lower explicit stigma, in the United Kingdom or United States than in South Korea (Kim et al., 2022), Japan (Keating et al., 2024; Someki et al., 2018), Lebanon (Gillespie-Lynch et al., 2019; Obeid et al., 2015), and Malaysia (de Vries et al., 2020).

Notably, the cross-cultural variation discovered here persisted even after controlling for relevant covariates (e.g. cultural orientation, gender, knowledge). This suggests that there may be additional factors that underpin the reduced or inaccurate knowledge and heightened stigma in Hong Kong, such as reduced openness to experience, or reduced presence of the neurodiversity movement (among other factors). With respect to the former, since being less open to new ideas and experiences is associated with higher explicit stigma (Gillespie-Lynch et al., 2019), and given that lower openness has been found in Hong Kong than in the United Kingdom (Chan, 2021), the elevated stigma in Hong Kong may be underpinned by lower openness to experience (see The Big Five Personality Traits; Digman, 1990). With respect to the latter, in Hong Kong, the dominant narratives shaping autism research and services are based on the medical model of deficits (Kwok & Kwok, 2020). While the endorsement of the neurodiversity movement in English-speaking Western countries has been associated with a positive shift in attitudes towards autism (den Houting, 2019; Kim & Gillespie-Lynch, 2023), limited exposure to the neurodiversity movement may contribute to the heightened stigma in Hong Kong (Cheng et al., 2023; Kim & Gillespie-Lynch, 2023; Turnock et al., 2022). Further research is necessary to determine the ‘additional’ factors that underpin cross-cultural variation in autism-related stigma and knowledge. By identifying these ‘additional’ factors, researchers will be able to construct comprehensive models that explain large amounts of variance in autism-related knowledge and stigma, paving the way for holistic interventions targeting multiple, highly influential factors.

An important finding from this study is that, although explicit stigma was higher in Hong Kong than in the United Kingdom and United States, there were no differences in implicit biases across countries. There are a number of potential interpretations of this finding. On one hand, it could be that individuals in the United Kingdom and the United States genuinely hold more positive explicit attitudes about autism than those in Hong Kong – perhaps due to being immersed within a culture where social norms have shifted to encourage individuals to be more aware and accepting of autistic people – while underlying, implicit attitudes are more durable, and thus taking longer to change. From a more cynical perspective, it could be that respondents from the United Kingdom and the United States simply reported more positive (explicit) attitudes as it is socially desirable in these countries to do so (more so than in Hong Kong), when in reality both explicit and implicit attitudes in these countries are negative. Although this latter interpretation is a possibility, here we found a positive relationship between explicit and implicit stigma, suggesting that the self-reported explicit attitudes reflect underlying implicit attitudes to some degree. Such a relationship would not be present if individuals provided false representations of their explicit attitudes about autism. As such, our results point towards genuine differences in explicit stigma, but not implicit biases, across countries.

Relatedly, our findings build upon those from Kim and colleagues (2023), who identified higher levels of implicit autism-related biases in South Korea than in the United States. Synthesizing these results, if disparities in implicit biases exist between South Korea and the United States but not between Hong Kong, the United Kingdom, and the United States, this could suggest higher levels of implicit biases in South Korea relative to both Hong Kong and the United Kingdom (in addition to the United States). Future research is necessary to confirm whether this is true.

Limitations

By examining autism-related knowledge and stigma across several countries, our sample is more culturally diverse than those traditionally employed in autism research. However, it is important to note that the samples from each of our countries may not be representative of their broader population with respect to gender, ethnicity, spoken language, and cultural orientation. First, since our samples were predominantly made up of cisgender females (Hong Kong: 63.0%; the United Kingdom: 65.8%; the United States: 54.9%), who tend to report lower levels of explicit stigma than males (Gillespie-Lynch et al., 2019; though see Kim et al., 2022), it is possible that levels of stigma in the broader population are underestimated here. Second, given that our samples were primarily White in the United Kingdom (70.0%) and the United States (64.8%), and Chinese in Hong Kong (81.5%), our results may not be representative of other ethnic groups. Third, while 69.3% of Hong Kong residents rate their English skills as sufficient to average for daily use (Hong Kong Census and Statistics Department, 2024), our whole sample were fluent in English and thus may not fully reflect the broader population. Finally, since our study focused on university students, who are thought to be more individualistic than those in the general population (Gillespie-Lynch et al., 2019), it is likely that some of the cross-cultural effects that we attempted to investigate (e.g., individualism-collectivism) were dampened here. Thus, future work should compare levels of autism-related stigma and knowledge across countries using representative general population samples. Such work should attempt to dismantle barriers to inclusion, ensuring that the samples represent the full range of diversity in each of the countries studied, for example, with respect to age, gender, ethnicity, level of education, and socioeconomic status.

It is also important to acknowledge the differences in recruitment procedures across countries. As mentioned previously, participants from Hong Kong were recruited via University of Hong Kong mailing lists, while those from the United Kingdom and the United States were recruited through Prolific. Consequently, our Hong Kong sample comprises students from a single institution, and thus may reflect specific institutional attitudes and norms, whereas the UK and US samples include students from a range of universities, resulting in more diverse institutional influences. Further research is necessary to examine whether university-specific practices influence knowledge about or stigma towards autism, and to investigate these factors across a broader range of universities in Hong Kong.

Finally, there is also ongoing debate surrounding the use of IAT tasks. While a full review of this literature is beyond the scope of this article, one concern is that IATs are vulnerable to contextual influences (Han et al., 2010) – though it is possible that implicit biases are genuinely shaped by context (Guinote et al., 2010; Jost, 2019). Another concern is that executive functions appear to impact IAT performance (Calanchini et al., 2014; Ito et al., 2015; Klauer et al., 2010), suggesting that IAT scores may not be a pure and uncensored reflection of implicit biases. A broader theoretical critique is that implicit biases, as measured by the IAT, may reflect exposure to societal stereotypes more than personal prejudice (Karpinski & Hilton, 2001; G. Mitchell & Tetlock, 2017). Nevertheless, previous studies indicate that IAT scores can predict behaviour (Jost, 2019; Jost et al., 2009), and in this study, implicit and explicit biases towards autism were related. If IAT scores are more indicative of exposure to stereotypes than personal prejudice, this would shift how we interpret individual results; however, the IAT would, nonetheless, still be a valuable tool for understanding autism stigma within societies.

Conclusion

Here, we aimed to (1) assess the contribution of knowledge and cultural orientation to both explicit stigma and implicit biases towards autism, and (2) compare levels of knowledge, explicit stigma, and implicit biases across Hong Kong, the United Kingdom, and the United States. Regarding our former aim, we found that explicit stigma was predicted by less accurate knowledge, lower horizontal collectivism, greater vertical individualism, and higher implicit biases. Implicit biases were directly predicted by age and explicit stigma, and indirectly predicted by vertical individualism (positively) and horizontal collectivism (negatively) via explicit stigma. Regarding our latter aim, we identified cross-cultural variation in autism-related knowledge and explicit stigma, but not implicit biases, even after accounting for covariates: students in Hong Kong displayed lower levels of knowledge about, and higher explicit stigma towards autism, than those in the United Kingdom and the United States. These findings underscore the crucial need to combat misconceptions about, and stigma towards autism, particularly in Hong Kong.

Supplemental Material

sj-docx-1-aut-10.1177_13623613241290565 – Supplemental material for Factors underlying differences in knowledge, explicit stigma and implicit biases towards autism across Hong Kong, the United Kingdom and the United States

Supplemental material, sj-docx-1-aut-10.1177_13623613241290565 for Factors underlying differences in knowledge, explicit stigma and implicit biases towards autism across Hong Kong, the United Kingdom and the United States by Yulin Cheng, Patrick Dwyer and Connor Tom Keating in Autism

Supplemental Material

sj-docx-2-aut-10.1177_13623613241290565 – Supplemental material for Factors underlying differences in knowledge, explicit stigma and implicit biases towards autism across Hong Kong, the United Kingdom and the United States

Supplemental material, sj-docx-2-aut-10.1177_13623613241290565 for Factors underlying differences in knowledge, explicit stigma and implicit biases towards autism across Hong Kong, the United Kingdom and the United States by Yulin Cheng, Patrick Dwyer and Connor Tom Keating in Autism

Footnotes

Acknowledgements

We thank the members of the autism community in Hong Kong, the United Kingdom, and the United States that provided feedback on our study. We are grateful to Dr Sophie Sowden-Carvalho for providing her insights on our initial funding and ethics applications.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project was supported via a Universitas 21 Graduate Collaborative Research Award and the Medical Research Council (MR/R015813/1).

ORCID iDs

Patrick Dwyer

Connor Tom Keating

Supplemental material

Supplemental material for this article is available online.

Notes

References

Baron

R. M.

Kenny

D. A.

(1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51(6), 1173. https://doi.org/10.1037/0022-3514.51.6.1173

Bogardus

E. S.

(1933). A social distance scale. Sociology & Social Research, 17, 265–271.

Bollen

K. A.

Pearl

(2013). Eight myths about causality and structural equation models. In Morgan

S. L.

(Ed.), Handbook of causal analysis for social research (pp. 301–328). Springer Netherlands. https://doi.org/10.1007/978-94-007-6094-3_15

Botha

Frost

D. M.

(2020). Extending the minority stress model to understand mental health problems experienced by the autistic population. Society and Mental Health, 10(1), 20–34. https://doi.org/10.1177/2156869318804297

Cage

Di Monaco

Newell

(2018). Experiences of autism acceptance and mental health in autistic adults. Journal of Autism and Developmental Disorders, 48, 473–484. https://doi.org/10.1007/s10803-017-3342-7

Cage

Di Monaco

Newell

(2019). Understanding, attitudes and dehumanisation towards autistic people. Autism, 23(6), 1373–1383. https://doi.org/10.1177/1362361318811290

Calanchini

Sherman

J. W.

Klauer

K. C.

Lai

C. K.

(2014). Attitudinal and non-attitudinal components of IAT performance. Personality and Social Psychology Bulletin, 40(10), 1285–1296. https://doi.org/10.1177/0146167214540723

Chan

B. C. L.

(2021). Multi-method exploration of physical activity, personality and wellbeing in older adults and university students in Hong Kong and the United Kingdom. https://doi.org/10.7488/era/1507

Cheng

Tekola

Balasubramanian

Crane

Leadbitter

(2023). Neurodiversity and community-led rights-based movements: Barriers and opportunities for global research partnerships. Autism, 27(3), 573–577. https://doi.org/10.1177/13623613231159165

10.

Cheung

M. W.

(2007). Comparison of approaches to constructing confidence intervals for mediating effects using structural equation models. Structural Equation Modeling: A Multidisciplinary Journal, 14(2), 227–246. https://doi.org/10.1080/10705510709336745

11.

Chopik

W. J.

Giasson

H. L.

(2017). Age differences in explicit and implicit age attitudes across the life span. The Gerontologist, 57(Suppl. 2), S169–S177. https://doi.org/10.1093/geront/gnx058

12.

Deaton

Cartwright

(2018). Understanding and misunderstanding randomized controlled trials. Social Science & Medicine, 210, 2–21. https://doi.org/10.1016/j.socscimed.2017.12.005

13.

den Houting

. (2019). Neurodiversity: An insider’s perspective. Autism, 23(2), 271–273. https://doi.org/10.1177/1362361318820762

14.

de Vries

Cader

Colleer

Batteux

Yasdiman

M. B.

Tan

Y. J.

Sheppard

. (2020). University students’ notion of autism spectrum conditions: A cross-cultural study. Journal of Autism and Developmental Disorders, 50, 1281–1294. https://doi.org/10.1007/s10803-019-04343-z

15.

Dickter

C. L.

Burk

J. A.

Zeman

J. L.

Taylor

S. C.

(2020). Implicit and explicit attitudes toward autistic adults. Autism in Adulthood, 2(2), 144–151. https://doi.org/10.1089/aut.2019.0023

16.

Digman

J. M.

(1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology, 41(1), 417–440. https://doi.org/10.1146/annurev.ps.41.020190.002221

17.

Dovidio

J. F.

Kawakami

Johnson

Howard

(1997). On the nature of prejudice: Automatic and controlled processes. Journal of Experimental Social Psychology, 33, 510–540. https://doi.org/10.1006/jesp.1997.1331

18.

Durand-Zaleski

Scott

Rouillon

Leboyer

(2012). A first national survey of knowledge, attitudes and behaviours towards schizophrenia, bipolar disorders and autism in France. BMC Psychiatry, 12(1), Article 128. https://doi.org/10.1186/1471-244X-12-128

19.

Dwyer

(2022). Stigma, incommensurability, or both? Pathology-first, person-first, and identity-first language and the challenges of discourse in divided autism communities. Journal of Developmental and Behavioral Pediatrics, 43(2), 111–113. https://doi.org/10.1097/DBP.0000000000001054

20.

Fletcher-Watson

Adams

Brook

Charman

Crane

Cusack

. . .Pellicano

(2019). Making the future together: Shaping autism research through meaningful participation. Autism, 23(4), 943–953. https://doi.org/10.1177/1362361318786721

21.

Fritz

M. S.

MacKinnon

D. P.

(2007). Required sample size to detect the mediated effect. Psychological Science, 18(3), 233–239. https://doi.org/10.1111/j.1467-9280.2007.01882.x

22.

Geelhand

Papastamou

Belenger

Clin

Hickman

Keating

C. T.

Sowden

(2023). Autism-related language preferences of French-speaking autistic adults: An online survey. Autism in Adulthood, 5(3), 275–288. https://doi.org/10.1089/aut.2022.0056

23.

Gernsbacher

M. A.

(2017). Editorial perspective: The use of person-first language in scholarly writing may accentuate stigma. Journal of Child Psychology and Psychiatry, 58(7), 859–861. https://doi.org/10.1111/jcpp.12706

24.

Gillespie-Lynch

Bisson

J. B.

Saade

Obeid

Kofner

Harrison

A. J.

. . .Jordan

(2022). If you want to develop an effective autism training, ask autistic students to help you. Autism, 26(5), 1082–1094. https://doi.org/10.1177/13623613211041006

25.

Gillespie-Lynch

Brooks

P. J.

Someki

Obeid

Shane-Simpson

Kapp

S. K.

. . .Smith

D. S.

(2015). Changing college students’ conceptions of autism: An online training to increase knowledge and decrease stigma. Journal of Autism and Developmental Disorders, 45, 2553–2566. https://doi.org/10.1007/s10803-015-2422-9

26.

Gillespie-Lynch

Daou

Sanchez-Ruiz

M. J.

Kapp

S. K.

Obeid

Brooks

P. J.

. . .Abi-Habib

(2019). Factors underlying cross-cultural differences in stigma toward autism among college students in Lebanon and the United States. Autism, 23(8), 1993–2006. https://doi.org/10.1177/1362361318823550

27.

Gillespie-Lynch

Kapp

S. K.

Brooks

P. J.

Pickens

Schwartzman

(2017). Whose expertise is it? Evidence for autistic adults as critical autism experts. Frontiers in Psychology, 8, Article 438.

28.

Gonsalkorale

Sherman

J. W.

Klauer

K. C.

(2009). Aging and prejudice: Diminished regulation of automatic race bias among older adults. Journal of Experimental Social Psychology, 45(2), 410–414. https://doi.org/10.1016/j.jesp.2008.11.004

29.

Goreczny

A. J.

Bender

E. E.

Caruso

Feinstein

C. S.

(2011). Attitudes toward individuals with disabilities: Results of a recent survey and implications of those results. Research in Developmental Disabilities, 32(5), 1596–1609. https://doi.org/10.1016/j.ridd.2011.02.005

30.

Greenwald

A. G.

McGhee

D. E.

Schwartz

J. L. K.

(1998). Measuring individual differences in implicit cognition: The Implicit Association Test. Journal of Personality and Social Psychology, 74, 1464–1480. https://doi.org/10.1037/0022-3514.74.6.1464

31.

Greenwald

A. G.

Nosek

B. A.

Banaji

M. R.

(2003). Understanding and using the implicit association test: I – An improved scoring algorithm. Journal of Personality and Social Psychology, 85, 197–216. https://doi.org/10.1037/0022-3514.85.2.197

32.

Grinker

R. R.

(2020). Autism, ‘stigma’, disability: A shifting historical terrain. Current Anthropology, 61(S21), S55–S67. https://doi.org/10.1086/705748

33.

Guinote

Willis

G. B.

Martellotta

(2010). Social power increases implicit prejudice. Journal of Experimental Social Psychology, 46(2), 299–307. https://doi.org/10.1016/j.jesp.2009.11.012

34.

Gunzler

Chen

Zhang

(2013). Introduction to mediation analysis with structural equation modeling. Shanghai Archives of Psychiatry, 25(6), 390. https://doi.org/10.3969/j.issn.1002-0829.2013.06.009

35.

Han

Scior

Avramides

Crane

(2022). A systematic review on autistic people’s experiences of stigma and coping strategies. Autism Research, 15(1), 12–26. https://doi.org/10.1002/aur.2652

36.

Han

H. A.

Czellar

Olson

M. A.

Fazio

R. H.

(2010). Malleability of attitudes or malleability of the IAT? Journal of Experimental Social Psychology, 46(2), 286–298. https://doi.org/10.1016/j.jesp.2009.11.011

37.

Hayes

A. F.

Scharkow

(2013). The relative trustworthiness of inferential tests of the indirect effect in statistical mediation analysis: Does method really matter? Psychological Science, 24(10), 1918–1927. https://doi.org/10.1177/0956797613480187

38.

Henderson

Evans-Lacko

Flach

Thornicroft

(2012). Responses to mental health stigma questions: The importance of social desirability and data collection method. The Canadian Journal of Psychiatry, 57(3), 152–160. https://doi.org/10.1177/070674371205700304

39.

Henderson

G. R.

Guzmán

Huff

Motley

C. M.

(2013). The Ian’s pizza tribe: Reconceptualizing cross-cultural research in the digital age. Journal of Business Research, 66(3), 283–287. https://doi.org/10.1016/j.jbusres.2011.08.007

40.

Hernández

A. V.

Steyerberg

E. W.

Habbema

J. D. F.

(2004). Covariate adjustment in randomized controlled trials with dichotomous outcomes increases statistical power and reduces sample size requirements. Journal of Clinical Epidemiology, 57(5), 454–460. https://doi.org/10.1016/j.jclinepi.2003.09.014

41.

Hinshaw

S. P.

Stier

(2008). Stigma as related to mental disorders. Annual Review of Clinical Psychology, 4, 367–393. https://doi.org/10.1146/annurev.clinpsy.4.022007.141245

42.

Hofmann

Gawronski

Gschwendner

Schmitt

(2005). A meta-analysis on the correlation between the Implicit Association Test and explicit self-report measures. Personality and Social Psychology Bulletin, 31(10), 1369–1385. https://doi.org/10.1177/0146167205275613

43.

Hofstede

(1984). Culture’s consequences: International differences in work-related values (Vol. 5). Sage Publications.

44.

Hofstede

(2001). Culture consequences. Sage Publications.

45.

Hong Kong Census and Statistics Department. (2024). Use of language in Hong Kong in 2021. Hong Kong Monthly Digest of Statistics.

46.

Ito

T. A.

Friedman

N. P.

Bartholow

B. D.

Correll

Loersch

Altamirano

L. J.

Miyake

(2015). Toward a comprehensive understanding of executive cognitive function in implicit racial bias. Journal of Personality and Social Psychology, 108(2), 187–218. https://doi.org/10.1037/a0038557

47.

Jensen

C. M.

Martens

C. S.

Nikolajsen

N. D.

Skytt Gregersen

Heckmann Marx

Goldberg Frederiksen

Hansen

M. S.

(2016). What do the general population know, believe and feel about individuals with autism and schizophrenia: Results from a comparative survey in Denmark. Autism, 20(4), 496–508. https://doi.org/10.1177/1362361315593068

48.

Jones

D. R.

DeBrabander

K. M.

Sasson

N. J.

(2021a). Effects of autism acceptance training on explicit and implicit biases toward autism. Autism, 25(5), 1246–1261. https://doi.org/10.1177/1362361320984896

49.

Jones

D. R.

Morrison

K. E.

DeBrabander

K. M.

Ackerman

R. A.

Pinkham

A. E.

Sasson

N. J.

(2021b). Greater social interest between autistic and non-autistic conversation partners following autism acceptance training for non-autistic people. Frontiers in Psychology, 12, Article 739147. https://doi.org/10.3389/fpsyg.2021.739147

50.

Jones

S. C.

(2022). Measuring the wrong thing the right way? Time to rethink autism research tools. Autism in Adulthood, 4(2), 104–109. https://doi.org/10.1089/aut.2021.0050

51.

Jones

S. C.

Akram

Gordon

C. S.

Murphy

Sharkie

(2021). Autism in Australia: Community knowledge and autistic People’s experiences. Journal of Autism and Developmental Disorders, 51, 3677–3689. https://doi.org/10.1007/s10803-020-04819-3

52.

Jost

J. T.

(2019). The IAT is dead, long live the IAT: Context-sensitive measures of implicit attitudes are indispensable to social and political psychology. Current Directions in Psychological Science, 28(1), 10–19. https://doi.org/10.1177/0963721418797309

53.

Jost

J. T.

Rudman

L. A.

Blair

I. V.

Carney

D. R.

Dasgupta

Glaser

Hardin

C. D.

(2009). The existence of implicit bias is beyond reasonable doubt: A refutation of ideological and methodological objections and executive summary of ten studies that no manager should ignore. Research in Organizational Behavior, 29, 39–69. https://doi.org/10.1016/j.riob.2009.10.001

54.

Karpinski

Hilton

J. L.

(2001). Attitudes and the Implicit Association Test. Journal of Personality and Social Psychology, 81(5), 774–788. https://doi.org/10.1037/0022-3514.81.5.774

55.

Keating

C. T.

(2021). Participatory autism research: How consultation benefits everyone. Frontiers in Psychology, 12, Article 713982. https://doi.org/10.3389/fpsyg.2021.713982

56.

Keating

C. T.

Hickman

Geelhand

Takahashi

Leung

Schuster

. . .Sowden

(2021). Global perspectives on autism acceptance, camouflaging behaviours and mental health in autism spectrum disorder: A registered report protocol. PLOS ONE, 16(12), Article e0261774. https://doi.org/10.1371/journal.pone.0261774

57.

Keating

C. T.

Hickman

Geelhand

Takahashi

Leung

Schuster

. . .Sowden

(2024). Cross-cultural variation in experiences of acceptance, camouflaging and mental health difficulties in autism: A registered report. PLOS ONE, 19(3), Article e0299824. https://doi.org/10.1371/journal.pone.0299824

58.

Keating

C. T.

Hickman

Leung

Monk

Montgomery

Heath

Sowden

(2023). Autism-related language preferences of English-speaking individuals across the globe: A mixed methods investigation. Autism Research, 16(2), 406–428. https://doi.org/10.1002/aur.2864

59.

Kim

S. Y.

(2020). The development and pilot-testing of the autism attitude acceptance scale: An instrument measuring autism acceptance. Autism in Adulthood, 2(3), 204–215. https://doi.org/10.1089/aut.2019.0066

60.

Kim

S. Y.

Cheon

J. E.

Gillespie-Lynch

Grandits

Kim

Y.-H.

(2023). Explicit stigma and implicit biases toward autism in South Korea versus the United States. Autism, 27(5), 1492–1507. https://doi.org/10.1177/13623613221140695

61.

Kim

S. Y.

Cheon

J. E.

Gillespie-Lynch

Kim

Y. H.

(2022). Is autism stigma higher in South Korea than the United States? Examining cultural tightness, intergroup bias, and concerns about heredity as contributors to heightened autism stigma. Autism, 26(2), 460–472. https://doi.org/10.1177/13623613211029520

62.

Kim

S. Y.

Gillespie-Lynch

(2023). Do autistic people’s support needs and non-autistic people’s support for the neurodiversity movement contribute to heightened autism stigma in South Korea vs. the US? Journal of Autism and Developmental Disorders, 53(11), 4199–4213. https://doi.org/10.1007/s10803-022-05739-0

63.

Kitchin

J. L.

Karlin

N. J.

(2022). Awareness and stigma of autism spectrum disorders in undergraduate students. Psychological Reports, 125(4), 2069–2087. https://doi.org/10.1177/00332941211014144

64.

Klauer

K. C.

Schmitz

Teige-Mocigemba

Voss

(2010). Understanding the role of executive control in the Implicit Association Test: Why flexible people have small IAT effects. Quarterly Journal of Experimental Psychology, 63(3), 595–619. https://doi.org/10.1080/17470210903076826

65.

Kuzminski

Netto

Wilson

Falkmer

Chamberlain

Falkmer

(2019). Linking knowledge and attitudes: Determining neurotypical knowledge about and attitudes towards autism. PLOS ONE, 14(7), Article e0220197. https://doi.org/10.1371/journal.pone.0220197

66.

Kwok

D. K.

(2020). More than comfort and discomfort: Emotion work of parenting children with autism in Hong Kong. Children and Youth Services Review, 118, 105456. https://doi.org/10.1016/j.childyouth.2020.105456

67.

Lane

D. M.

Scott

Hebl

Guerra

Osherson

Zimmer

(2013). Introduction to statistics: An interactive e-book. University of Houston.

68.

Ling

C. Y.

Mak

W. W.

Cheng

J. N.

(2010). Attribution model of stigma towards children with autism in Hong Kong. Journal of Applied Research in Intellectual Disabilities, 23(3), 237–249. https://doi.org/10.1111/j.1468-3148.2008.00456.x

69.

MacKinnon

D. P.

Lockwood

C. M.

Williams

(2004). Confidence limits for the indirect effect: Distribution of the product and resampling methods. Multivariate Behavioral Research, 39(1), 99–128. https://doi.org/10.1207/s15327906mbr3901_4

70.

Mitchell

Tetlock

P. E.

(2017). Popularity as a poor proxy for utility: The case of implicit prejudice. In Lilienfeld

S. O.

Waldman

I. D.

(Eds.), Psychological science under scrutiny: Recent challenges and proposed solutions. Wiley. https://doi.org/10.1002/9781119095910.ch10

71.

Mitchell

G. E.

Locke

K. D.

(2015). Lay beliefs about autism spectrum disorder among the general public and childcare providers. Autism, 19(5), 553–561. https://doi.org/10.1177/1362361314533839

72.

Nevill

R. E.

White

S. W.

(2011). College students’ openness toward autism spectrum disorders: Improving peer acceptance. Journal of Autism and Developmental Disorders, 41, 1619–1628. https://doi.org/10.1007/s10803-011-1189-x

73.

C. S. M.

S. S. L.

(2022). A qualitative study on the experience of stigma for Chinese parents of children with autism spectrum disorder. Scientific Reports, 12(1), 19550. https://doi.org/10.1038/s41598-022-23978-0

74.

Obeid

Daou

DeNigris

Shane-Simpson

Brooks

P. J.

Gillespie-Lynch

(2015). A cross-cultural comparison of knowledge and stigma associated with autism spectrum disorder among college students in Lebanon and the United States. Journal of Autism and Developmental Disorders, 45, 3520–3536. https://doi.org/10.1007/s10803-015-2499-1

75.

Oyserman

(2017). Culture three ways: Culture and subcultures within countries. Annual Review of Psychology, 68(1), 435–463. https://doi.org/10.1146/annurev-psych-122414-033617

76.

Papadopoulos

Lodder

Constantinou

Randhawa

(2019). Systematic review of the relationship between autism stigma and informal caregiver mental health. Journal of Autism and Developmental Disorders, 49, 1665–1685. https://doi.org/10.1007/s10803-018-3835-z

77.

Park

Lee

Kim

C. E.

(2018). Korean adults’ beliefs about and social distance toward attention-deficit hyperactivity disorder, Tourette syndrome, and autism spectrum disorder. Psychiatry Research, 269, 633–639. https://doi.org/10.1016/j.psychres.2018.08.023

78.

Qualtrics. (2005). Provo, Utah, USA (Version: March 2022). https://www.qualtrics.com

79.

Rao

Horton

R. A.

Tsang

H. W.

Shi

Corrigan

P. W.

(2010). Does individualism help explain differences in employers’ stigmatizing attitudes toward disability across Chinese and American cities? Rehabilitation Psychology, 55, 351–359. https://doi.org/10.1037/a0021841

80.

Rong

Zhang

Liu

Zhao

Song

Gao

(2022). A bibliometrics analysis and visualization of autism spectrum disorder. Frontiers in Psychiatry, 13, Article 884600. https://doi.org/10.3389/fpsyt.2022.884600

81.

Saade

Bean

Y. F.

Gillespie-Lynch

Poirier

Harrison

A. J.

(2021). Can participation in an online ASD training enhance attitudes toward inclusion, teaching self-Efficacy and ASD knowledge among preservice educators in diverse cultural contexts? International Journal of Inclusive Education, 28(2), 161–176. https://doi.org/10.1080/13603116.2021.1931716

82.

Sasson

N. J.

Faso

D. J.

Nugent

Lovell

Kennedy

D. P.

Grossman

R. B.

(2017). Neurotypical peers are less willing to interact with those with autism based on thin slice judgments. Scientific Reports, 7(1), 1–10. https://doi.org/10.1038/srep40700

83.

Shavitt

Lalwani

A. K.

Zhang

Torelli

C. J.

(2006). The horizontal/vertical distinction in cross-cultural consumer research. Journal of Consumer Psychology, 16(4), 325–342. https://doi.org/10.1207/s15327663jcp1604_3

84.

Singelis

T. M.

Triandis

H. C.

Bhawuk

D. P.

Gelfand

M. J.

(1995). Horizontal and vertical dimensions of individualism and collectivism: A theoretical and measurement refinement. Cross-Cultural Research, 29(3), 240–275. https://doi.org/10.1177/106939719502900302

85.

Someki

Torii

Brooks

P. J.

Koeda

Gillespie-Lynch

(2018). Stigma associated with autism among college students in Japan and the United States: An online training study. Research in Developmental Disabilities, 76, 88–98. https://doi.org/10.1016/j.ridd.2018.02.016

86.

Stewart

B. D.

Von Hippel

Radvansky

G. A.

(2009). Age, race, and implicit prejudice: Using process dissociation to separate the underlying components. Psychological Science, 20(2), 164–168. https://doi.org/10.1111/j.1467-9280.2009.02274.x

87.

Stier

Hinshaw

S. P.

(2007). Explicit and implicit stigma against individuals with mental illness. Australian Psychologist, 42, 106–117. https://doi.org/10.1080/00050060701280599

88.

Stone

W. L.

(1987). Cross-disciplinary perspectives on autism. Journal of Pediatric Psychology, 12(4), 615–630. https://doi.org/10.1093/jpepsy/12.4.615

89.

Sweileh

W. M.

Al-Jabi

S. W.

Sawalha

A. F.

Zyoud

S. H.

(2016). Bibliometric profile of the global scientific research on autism spectrum disorders. SpringerPlus, 5(1), 1480. https://doi.org/10.1186/s40064-016-3165-6

90.

Thomas

G. M.

(2021). Dis-mantling stigma: Parenting disabled children in an age of ‘neoliberal-ableism’. The Sociological Review, 69(2), 451–467. https://doi.org/10.1177/003802612096348

91.

Triandis

H. C.

(1988). Collectivism v. individualism: A reconceptualisation of a basic concept in cross-cultural social psychology. In Verma

G. K.

Bagley

(Eds.), Cross-cultural studies of personality, attitudes and cognition (pp. 60–95). Palgrave Macmillan UK. https://doi.org/10.1007/978-1-349-08120-2_3

92.

Triandis

H. C.

Gelfand

M. J.

(1998). Converging measurement of horizontal and vertical individualism and collectivism. Journal of Personality and Social Psychology, 74(1), 118. https://doi.org/10.1037/0022-3514.74.1.118

93.

Turnock

Langley

Jones

C. R.

(2022). Understanding stigma in autism: A narrative review and theoretical model. Autism in Adulthood, 4(1), 76–91. https://doi.org/10.1089/aut.2021.0005

94.

Valente

M. J.

Gonzalez

Miočević

MacKinnon

D. P.

(2016). A note on testing mediated effects in structural equation models: Reconciling past and current research on the performance of the test of joint significance. Educational and Psychological Measurement, 76(6), 889–911. https://doi.org/10.1177/0013164415618992

95.

White

Hillier

Frye

Makrez

(2019). College students’ knowledge and attitudes towards students on the autism spectrum. Journal of Autism and Developmental Disorders, 49, 2699–2705. https://doi.org/10.1007/s10803-016-2818-1

96.

Wilms

Mäthner

Winnen

Lanwehr

(2021). Omitted variable bias: A threat to estimating causal relationships. Methods in Psychology, 5, 100075. https://doi.org/10.1016/j.metip.2021.100075

97.

Wooldridge

J. M.

(2012). Introductory econometrics: A modern approach (upper level economics titles) (pp. 673–690). Southwestern College Publishing.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB