Sage Journals: Discover world-class research

Abstract

An autism spectrum condition is considered a lifelong condition. Affected individuals typically experience poorer psychosocial outcomes compared to non-autistic individuals. However, in Southeast Asian countries such as Malaysia, research about autism spectrum conditions beyond the early childhood context is scarce, and therefore, the prevalence and psychosocial outcomes of young adults with autism spectrum conditions remain largely unknown. This study aims to explore self-reported autistic traits among university students in Malaysia, and their relation to emotion regulation, stress, self-efficacy and quality of life. Data from 2732 students revealed that 0.3% of the students reported having an autism spectrum condition diagnosis, while 2.5% reported that they suspected having an autism spectrum condition, but did not obtain a formal diagnosis. This indicates that an estimated 2.8% of students might have a diagnosed or suspected autism spectrum condition. These two categories of students were found to have poorer self-efficacy, higher perceived stress and poorer quality of life, as compared to their peers. However, these students were not significantly different from their peers in emotion regulation. Overall, the findings pointed to negative psychosocial outcomes associated with autism spectrum condition and culture-specific associations between autistic traits and psychosocial outcomes.

Lay abstract

Most research on autism spectrum conditions is done on young children and not as much on young adults with autism spectrum conditions. Because of this, not much is known about how common autism spectrum conditions are in young adults and how they affect their mental and social health, especially in Southeast Asia. Based on self-reports, 2.8% of the 2732 university students we looked at in Malaysia might have been diagnosed or thought to have an autism spectrum condition. Students who said they had or thought they had autism spectrum conditions had lower self-efficacy, more stress and a lower quality of life than students who said they did not have autism spectrum conditions. The results showed that university students with autism spectrum condition are more likely to have bad psychological and social outcomes than their peers without autism spectrum condition. Also, the link between autistic traits and psychosocial outcomes could depend on the culture.

Keywords

autism spectrum condition prevalence psychosocial outcomes university students

Prevalence and psychosocial outcomes of autism spectrum condition among university students in Malaysia

Autism spectrum condition (henceforth, ASC) is a neurodevelopmental condition associated with social communication impairments and repetitive, stereotypical behaviours (American Psychiatric Association, 2013). Based on epidemiological studies conducted over the past 50 years, the World Health Organization (2021) estimated that 1 in 160 individuals (0.6%) worldwide has an ASC. However, prevalence estimates of ASC vary across and within geographical areas, countries, years of study and sources of data (Chiarotti & Venerosi, 2020). Among the main sources to estimate the prevalence of ASC are administrative data, ad hoc studies and surveys (Chiarotti & Venerosi, 2020). More recently, data simulation techniques in a combined pool of unpublished surveillance data were used to estimate ASC prevalence among adults, showing that an estimated 2.21% of American adults aged 18 years and above have ASC (Dietz et al., 2020). This estimation is consistent with the latest prevalence estimation of 2.3% (1 in 44) in the United States (Maenner et al., 2021). However, the global prevalence of ASCs is not uniform with several countries reporting a lower frequency (see Salari et al., 2022 for a review).

Many individuals with ASC today, especially those who are already adults, may still be juggling their daily lives without a diagnosis. The reason that many adults with ASC do not have a diagnosis could be due to the fact that ASC was diagnosed infrequently until it was captured under the category of Pervasive Developmental Disorder in the Diagnostic and Statistical Manual of Mental Disorders (3rd ed., DSM-III) in 1980 (Rosen et al., 2021). Autistic people who were born before 1980 might not have been diagnosed at an early age but later in life (Brugha et al., 2011; Lai & Baron-Cohen, 2015; Robison, 2019). Lai and Baron-Cohen (2015) refer to autistic adults who were previously excluded from a diagnosis of classic autism as the ‘lost generation’. A systematic review indicated that late-diagnosed autistic adults tend to have limited intellectual issues and exhibit social interaction difficulties rather than restrictive/repetitive behaviours and interests (Huang et al., 2020). There are many possible reasons for late or lack of diagnoses, such as lack of prior knowledge of ASC (Robison, 2019), deliberately keeping ASC diagnosis out of one’s medical record (Robison, 2019), overlapping behaviour with other psychiatric diagnoses (Lai & Baron-Cohen, 2015) or the lack of ASC awareness training for healthcare professionals to accurately diagnosis ASC (Au-Yeung et al., 2019).

Globally, there is a stigma attached to ASC and therefore, ASC could be a basis for discrimination (Robison, 2019). Consequently, some autistic adults are aware of their condition but choose to fly under the radar due to perceived threats, such as denial of education and vocational opportunities associated with the diagnosis (Robison, 2019). However, amassing evidence showed that late diagnosis, suspected ASC and even abstaining from ASC disclosure are typically associated with poorer psychosocial outcomes (Bargiela et al., 2016), which are increasingly noted in the context of higher education (Cage & Howes, 2020).

University students with ASC reported facing certain challenges, which could be related to autistic traits and other related characteristics, such as perfectionist tendencies, procrastination, failure to identify strengths and weaknesses, or overreliance on parents for decision-making (Anderson, 2020). Diagnostic disclosure is a requirement to obtain disability support services in universities, but many students who might meet diagnostic criteria for an ASC do not seek out a diagnosis or limit their disclosure to support service personnel and faculty staff only (Thompson-Hodgetts et al., 2020). In some contexts, the reluctance to disclose could be due to the fear of discrimination and stigma (Thompson-Hodgetts et al., 2020); while in some limited-resource contexts, the reluctance to disclose may be due to family influence or the lack of facilities to obtain a formal diagnosis (Farsinejad et al., 2022).

ASC in higher education context

Reduced health support for adults with ASC beyond the childhood context is a global issue. Pertaining to this, the identification of ASC beyond the childhood context and the related psychosocial needs are important for identifying support needs, signposting appropriate treatments, providing access to services, reducing self-criticism and fostering a positive identity in those affected (Mandy et al., 2018). Young adults with ASC often experience a high number of job changes, reduced working hours and are overrepresented in low-paid positions (Harvery et al., 2021), with fewer than half of them maintaining a job (Solomon, 2020). Moreover, poor psychosocial outcomes including a higher rate of psychiatric disorders and unmet mental health needs are reported (Hossain et al., 2020; Lugo-Marin et al., 2019; Rosen et al., 2018). Young adults with ASC often reported feeling daunted by the added difficulties their autistic traits posed, which set them up for ongoing struggles (Sosnowy et al., 2018, 2019).

In the context of higher education, attending university may present many environmental challenges for young adults with ASC (Jansen et al., 2017), including navigating to and from university independently (Thompson et al., 2019) and studying in a lecture hall, which is comparatively much more sensory overwhelming than a school classroom with a more structured layout and schedule (Cai & Richdale, 2016). Students with ASC may experience over-sensitivity to changes, stereotypical, repetitive movements and problems with verbal and non-verbal communication, distinguishing gist from details, planning, organizing, global information processing, social interaction, handling stress, cognitive flexibility, directing and shifting attention, and applying learning strategies (Jansen et al., 2017). The severity of autistic symptoms, together with executive functioning skills, correlates with the academic progress of young adults with ASC in higher education (Dijkhuis et al., 2020) and later the transition to work (Gurbuz et al., 2019).

It is becoming more and more apparent that the struggles of individuals with ASC in higher education are real (Bakker et al., 2019; Cai & Richdale, 2016; Dijkhuis et al., 2020; Jansen et al., 2017; McLeod et al., 2019; Thompson et al., 2019). Without properly addressing these challenges, young adults with ASC might be exposed to an increased risk of negative social experiences, such as bullying and social isolation at the university, causing long-lasting deleterious effects (Maiano et al., 2016; McLeod et al., 2019). In some instances, such negative experiences are known to trigger episodes of depression and acute psychotic attacks (Park et al., 2019; Sampson et al., 2021).

Self-reported ASC traits and psychosocial outcomes

In recent years, self-report methods have increasingly been used to profile autistic traits among university students globally (Lundqvist & Lindner, 2017). For countries in Asia, such as Malaysia, researchers have used the self-report Autism-Spectrum Quotient (AQ) (Baron-Cohen et al., 2001) as a way to assess autistic traits in the general population (Chee & de Vries, 2022; de Vries et al., 2020; Freeth et al., 2013). The AQ (Baron-Cohen et al., 2001) is one of the most widely used self-assessment scales for the purpose of autistic traits screening and profiling to date (Lundqvist & Lindner, 2017). The AQ has high internal consistency and test–retest reliability, especially when the 4-point Likert-type scale scoring was applied (Stevenson & Hart, 2017) and the shorter forms are used (Lundqvist & Lindner, 2017; van Rentergem et al., 2019). The availability of self-reporting scales such as the AQ allows researchers to compare the unique characteristics of individuals who received early and late diagnoses of ASC to non-ASC counterparts (e.g. Oomen et al., 2021). Although ASC is a lifelong condition, different sets of life experiences and social expectations in adulthood could shape the manifestation of autistic behaviours. The AQ can give a profile of autistic behaviours in adulthood.

The original AQ (Baron-Cohen et al., 2001) has been abridged to the AQ-28 (Hoekstra et al., 2011). This scale consists of five factors: social skills, routine, switching, and imagination, and numbers and patterns, which resonate with difficulties experienced by autistic students. Although many young adults with ASC are highly motivated to interact with others, they may lack the social skills to create and maintain friendships (Fletcher-Watson et al., 2013; Jobe & White, 2007), leading to feelings of loneliness (Jobe & White, 2007). Many of them prefer routine and stability and they could experience a high level of emotional fluctuations when coping with changes (Lei & Russell, 2021). At the same time, autistic students in universities were found to exhibit poorer attention switching which could be related to poorer inhibitory control and attentional flexibility (Mostert-Kerckhoffs et al., 2015; Siebes et al., 2018). Individuals with ASC were also found to have a lower imagination score which was associated with obsessive-compulsive symptoms (Doi et al., 2021). In comparison with these four sub-scales, attention to detail, related to the factor numbers and patterns, has been less associated with negative psychosocial outcomes. Some studies linked attention to detail to cognitive advantages and savant skills in autistic individuals (Baron-Cohen et al., 2001; Hughes et al., 2018), and even a negative association with social-communicative difficulties among Asian autistic individuals (Ward et al., 2021). Furthermore, there is evidence that the sub-scale of factor numbers and patterns (i.e. attention to details) was not able to differentiate between non-autistic and autistic individuals (Kurita et al., 2005).

Most studies on university students with ASC are limited to Western countries, and studies involving university students with ASC in Asia are limited. It is important to investigate the types of experiences and issues faced by university students with ASC in higher education in different cultural contexts and countries, and the associations of autistic traits on psychosocial outcomes, including emotion regulation, stress, self-efficacy and quality of life (QoL). This is particularly important because autistic people with higher cognitive abilities may have higher anxiety and depression rates (Sterling et al., 2008). Therefore, the current study aims to answer the following research questions: (1) What is the self-reported ASC rate in university students? (2) What are the psychosocial outcomes of ASC students in comparison with non-ASC students? (3) Are autistic traits related to psychosocial outcomes?

Methodology

Procedure

An online survey was done to study autistic traits and psychosocial outcomes of students from two public and two private universities in Malaysia. Human ethical approval was obtained from all the participating institutions which abide by the Declaration of Helsinki research ethics guidelines (approval code of host institution: 21030283) for the survey. Between June and July 2021, online questionnaires (Qualtrics and Microsoft forms) were distributed through emails, social media and WhatsApp to undergraduate and postgraduate students. After the information sheet and consent form, participants filled in the questionnaires. On average, participation took 19 min. Participants who submitted the questionnaires were entitled for a lucky draw. Twelve cash vouchers were given away in the lucky draw.

Community involvement

The research was led by academicians and a representative from a local ASC support group. The community member in the team provided important feedback to ensure that the online questionnaire design is suitable for diverse-ability individuals.

Participants

After removing duplicated data (n = 66) and data from non-university students (n = 6), a dataset from 2732 undergraduate and postgraduate students was included for data analysis. Their mean age is 22.98 years (SD = 3.853). See Table 1 for other demographic information.

Table 1.

Demographic details of respondents (N = 2732).

		N	%
Gender	Male	732	26.8
	Female	1943	71.1
	Prefer not to say	57	2.1
Level of study	Foundation	33	1.2
	Undergraduate	2332	85.4
	Postgraduate	367	13.4
Mode of study	Full-time	2633	96.4
Mode of study	Part-time	67	2.5
Year of study	First year	1061	38.8
	Second year	683	25.0
	Third year	469	17.2
	Fourth year	484	17.7
	>Beyond	35	1.2
Nationality	Malaysian	2615	95.7
Nationality	Non-Malaysian	117	4.3
Ethnicity	Malay	1664	60.9
	Chinese	775	28.4
	Indian	182	6.7
	Others	106	3.9
State of origin			Selangor (17.3), Pulau Pinang (12.8), Perak (9.9), Johor (9.5), Kelantan (9.0), Kedah (8.8), Kuala Lumpur (5.9), Sabah (5.2), Pahang (4.8), Terengganu (4.4), Sarawak (4.3), Negeri Sembilan (3.8), Melaka (3.1), Perlis (1.1), Putrajaya (0.1)

Research instruments

The online survey included demographic information (see Table 1), self-reported health information, and seven instruments as listed below. Two language versions (namely, Malay language and English) were made available so that the respondents could choose their preferred language. Although not all scales are thoroughly validated in autistic samples, we decided on the current scales because of their overall applicability to the research. Moreover, the majority of our sample was in fact non-autistic; hence, we aimed to look for questionnaires that were appropriate for general population samples, and as much as possible also for autistic samples.

AQ-28

The AQ-28 is a 28-item self-reported measure of autistic traits (Hoekstra et al., 2011) based on the original AQ-50 (Baron-Cohen et al., 2001). In AQ-28, autistic traits were classified as social traits (social skills, routine, switching and imagination) and non-social traits (factor numbers and patterns) (Hoekstra et al., 2011). We used a 4-point Likert-type-scale (from 1 = definitely agree to 4 = definitely disagree) as this improves the range of reliable measurement (Murray et al., 2016). The Malay (Chee & de Vries, 2022) and English (Hoekstra et al., 2011) versions of AQ-28 were administered. Adequate psychometric properties have been reported for both scales, Cronbach’s alpha = 0.87 (Chee & de Vries, 2022) and Cronbach’s alpha between 0.77 and 0.86 for the sub-scales (Hoekstra et al., 2011).

Emotion Regulation Questionnaire

The 10 items of the Emotion Regulation Questionnaire (Gross & John, 2003) measure the respondents’ tendency to regulate their emotions, using a 7-point Likert-type scale (from 1 = strongly disagree to 7 = strongly agree). This scale was found to have strong psychometric properties when used with 508 general community members from the United States, Cronbach’s alpha between 0.75 and 0.90 (Preece et al., 2020, 2021).

Perceived Stress Scale

The 10 items of the Perceived Stress Scale (Cohen et al., 1983) measure the experienced level of stress in one’s life, using a 5-point Likert-type scale (from 0 = never to 4 = very often). The Perceived Stress Scale shows adequate internal consistency across different populations, Cronbach’s alpha between 0.67 and 0.91 (Liu et al., 2020).

The General Self-Efficacy Scale

The General Self-Efficacy Scale (Jerusalem & Schwarzer, 1995) assesses a general sense of perceived self-efficacy with the aim to predict efficacy in coping with daily hassles and stressful life events, with 10 items using a 4-point Likert-type scale (from 1 = not true at all to 4 = exactly true). Internal consistency, Cronbach’s alpha between 0.84 and 0.90, was reported (Lazić et al., 2021).

The Satisfaction with Life Scale

QoL was measured with two questionnaires. The Satisfaction with Life Scale (Diener et al., 1985) measures global cognitive judgements of one’s life satisfaction, using five items scored on a 7-point Likert-type scale. This scale generated good internal consistency, Cronbach’s alpha between 0.88 and 0.94, in past studies (Bagherzadeh et al., 2018; Kjell & Diener, 2021).

The Autism-Specific QoL

The autism-specific QoL (McConachie et al., 2018) scale is the other QoL scale which measures the extent of challenging issues that adults with ASC may experience in their lives, with nine items scored on a 5-point Likert-type scale. The Cronbach’s alpha was recorded as 0.82 in the original study (McConachie et al., 2018).

Results

Data analysis

To answer the first research question, we grouped the data based on self-reported ASC (No, I do not have ASC; I do not know; I prefer not to say; I suspect that I might have ASC, but it has never been confirmed; Yes, I have an ASC diagnosis). The AQ data were normally distributed for each group, as assessed by Q–Q plot. Due to the vast difference in the sub-group sample sizes, Welch’s analyses of variance (ANOVAs) were conducted to examine the sub-group differences in self-reported medical and ASC conditions. Next, considering that Malaysia is a country with relatively many undiagnosed adults with ASC (as described in the introduction), the data were then divided into only two groups: ASC group refers to self-reported suspecting or having ASC, while non-ASC group refers to those who confirmed not having ASC. Again, due to the uneven sample sizes, Welch approximation T tests were conducted to evaluate between-group differences in AQ sub-scale scores. To answer the second research question, whether the ASC group has more adverse psychosocial outcomes, we did Welch approximation T tests to examine between-group emotion regulation, self-perceived stress, self-efficacy and QoL differences. Finally, regression analyses were conducted in the full sample to identify the dimensions of autistic traits that might predict self-perceived emotion regulation, stress, self-efficacy and QoL.

Self-reported ASC rate

Table 2 shows that 2.5% of students reported that they suspected they might have ASC (suspected ASC) and 0.3% reported having a diagnosis of ASC (diagnosed ASC). However, 78% reported that they did not have ASC (non-ASC). Another 18.3% reported that they did not know and 1.0% preferred not to say. Suspected (5.9%) and diagnosed ASC students (37.5%) reported significantly higher rates of medical conditions than the other groups (0%–2.2%), χ²(1, n = 2732) = 65.377, p < 0.001. Among those who reported having had medical conditions, 80% and above those with suspected and diagnosed ASC reported having a mental-related medical condition; while the frequencies of mental-related medical condition in the other groups were only 0%–32.8%. The differences were statistically significant, χ²(1, n = 85) = 9.086, p = 0.028.

Table 2.

Self-reported ASC and the corresponding medical conditions.

	No, I do not have autism (Non-ASC)	I do not know	I prefer not to say	I suspect that I might have autism, but it has never been confirmed (suspected ASC)	Yes, I have an autism diagnosis (diagnosed ASC)
Self-reported ASC, % (n)	78.0 (2130)	18.3 (499)	1.0 (27)	2.5 (68)	0.3 (8)
Self-reported disability/medical condition, %, χ²(1, n = 2732) = 65.377, p < 0.001
No	96.9	93.6	96.3	88.2	62.5
Maybe	0.9	2.4	3.7	5.9	0
Yes	2.2	4.0	0	5.9	37.5
Types of disability/medical condition (exclude ASC), %, χ²(1, n = 85) = 9.086, p = 0.028
Physical disability	3.6	4.5	0	0	0
Medical condition	66.1	54.4	100.0	20.0	0
Injury	7.1	9.1	0	0	0
Mental condition	23.2	31.8	0	80.0	100.0
AQ scores, F(4, 2727) = 36.809, p < 0.001
M (SD)	65.33 (7.222)	68.77 (7.378)	68.52 (5.605)	71.93 > 70 (8.638)	74.38 > 70 (10.127)
95% confidence interval for mean	65.03, 65.54	68.12, 69.42	66.30, 70.74	69.84, 74.02	65.91, 82.84

ASC: autism spectrum condition.

Table 2 also shows the self-reported ASC responses and the AQ-28 mean scores (AQ scores) for each group. The suspected ASC (AQ score = 71.93) and diagnosed ASC groups (AQ score = 74.38) had AQ scores above the strict cut-off of 70 (Hoekstra et al., 2011), indicating a strong likelihood of having an ASC. High sensitivity of 0.94 and specificity of 0.91 were previously reported by Hoekstra et al. (2011) for this cut-off score. Welch’s ANOVA results indicated statistically significant differences in the AQ mean scores across the different self-reported ASC groups, F(4, 2727) = 36.809, p < 0.001, d = 0.05. Post hoc comparisons using Games-Howell test indicated that students in suspected ASC and diagnosed ASC groups are not statistically differentiable from one another (mean difference = 2.449, p = 0.961), suggesting the homogeneity of both groups.

Autistic traits

Given the similar AQ scores, and to increase statistical power for the subsequent analyses, the suspected (n = 68) and diagnosed ASC (n = 8) groups were unified as ‘ASC group’ while using non-ASC group as a control. The other two groups (‘do not know’ and ‘prefer not to say’ groups) were excluded from these analysis. Figure 1 summarizes the comparison of AQ scores between the ASC and non-ASC group. The ASC group reported significantly higher autistic traits in the domains of social skills (p < 0.001), routine (p = 0.037) and attention switching (p < 0.001) than the non-ASC group, with effect sizes of 0.003–0.032. However, the ASC group was not different from the non-ASC group in the domains of imagination and factor numbers and patterns, p > 0.05.

Figure 1.

Comparison of AQ scores between ASC (n = 76) and non-ASC groups (n = 2130).

Comparison of emotion regulation, perceived stress, self-efficacy and QoL

Next, the psychosocial outcomes of ASC and non-ASC students were compared. Statistically significant differences in perceived stress (p < 0.001), self-efficacy (p < 0.001) and general QoL (p = 0.001), with effect sizes of d = 0.005–0.017, were less favourable outcomes which were reported for the ASC group. There were no differences in emotion regulation (p = 0.144) and ASC-specific QoL (p = 0.353) (Figure 2).

Figure 2.

Comparison of emotion regulation, perceived stress, self-efficacy and satisfaction of life between ASC (n = 76) and non-ASC groups (n = 2130).

Predictors of psychosocial outcomes

The last analyses were to identify autistic traits that could predict psychosocial outcomes in the whole sample of university students in this study (N = 2732). Table 3 shows the results of multiple linear regression. The results revealed variabilities in the ways autistic traits relate to psychosocial outcomes. Switching seems to be a critical aspect of autistic traits which relates to all psychosocial outcomes (p < 0.001). This is followed by social skills and routine which each related to four out of five psychosocial outcomes. Social skills did not affect emotion regulation (p = 0.967), while routine did not affect the general QoL (p = 0.258). Imagination was found to only weakly relate to emotion regulation (p = 0.008), self-efficacy (p < 0.001) and ASC-related QoL (p = 0.002). The factor numbers and patterns related to self-efficacy, emotion regulation and general QoL, though positively. It is also noted that autistic traits accounted for relatively less variance in emotion regulation than in other identified psychosocial outcomes (adjusted R² value of 0.058).

Table 3.

Results of multiple regression analysis in the full sample (n = 2732) by AQ factors (add the descriptive information).

	t	p	β	M	SD	F	df	p	Adj. R²
Self-efficacy				29.89	4.805	212.172	5, 2726	0.000	0.279
Social skills**	−9.576	0.000	−0.176
Routine**	−3.441	0.001	−0.061
Switching**	−13.976	0.000	−0.266
Imagination**	−10.520	0.000	−0.181
Factor numbers and patterns**	6.766	0.000	0.115
Emotion regulation				51.34	8.652	35.890	5, 2726	0.000	0.058
Social skills	−0.041	0.967	−0.001
Routine**	−3.682	0.000	−0.074
Switching**	−4.277	0.000	−0.093
Imagination**	−2.643	0.008	−0.052
Factor numbers and patterns**	7.877	0.000	0.152
Perceived stress				29.07	6.037	117.529	5, 2726	0.000	0.176
Social skills**	10.252	0.000	0.202
Routine**	6.503	0.000	0.123
Switching**	11.637	0.000	0.237
Imagination	−1.635	0.102	−0.030
Factor numbers and patterns	1.781	0.075	0.032
QoL (general)				22.20	6.126	72.857	5, 2726	0.000	0.116
Social skills**	10.065	0.000	−0.205
Routine	−1.130	0.258	−0.022
Switching**	−8.472	0.000	−0.178
Imagination	0.705	0.481	0.013
Factor numbers and patterns*	2.616	0.009	0.049
QoL (autism-specific)				29.19	4.82	62.316	5, 2726	0.000	0.101
Social skills**	−9.738	0.000	−0.200
Routine**	−2.866	0.004	−0.057
Attention switching**	−5.603	0.000	−0.119
Imagination**	−3.044	0.002	−0.058
Factor numbers and patterns	0.039	0.969	0.001

SD: standard deviation.

p < 0.05, **p < 0.01.

Discussion

This study aimed to explore self-reported autistic traits among university students, and their relation to emotion regulation, stress, self-efficacy and QoL. In our sample, out of every nine university students who suspected that they might have ASC, only one has a formal diagnosis. The collectivist culture in Southeast Asia might pose a barrier to obtaining a formal diagnosis of ASC (Ilias et al., 2018; Smith et al., 2021). Higher levels of stigma have been reported in Asia as compared to the United States (Yu et al., 2020). A better understanding of ASC and building of trust between professionals and community members are important efforts to minimize negative risks associated with a late or missed diagnosis of ASC, especially among ASC individuals with an average to high IQ.

Both the suspected and diagnosed ASC students scored above the stringent cut-off score of 70 on the AQ-28 (Hoekstra et al., 2011). The AQ scores obtained in this study are higher than the published norms of 53–60 (Hoekstra et al., 2011). This finding is consistent with the findings from past studies in Malaysia that AQ scores obtained in Malaysia seem to be higher than those collected in the United Kingdom (Chee & de Vries, 2022; Freeth et al., 2013). The current study showed that 2.8% of the students suspected having an ASC or had an ASC diagnosis. This is very close to the adult ASC estimation of 2.21% for American adults aged 18 years and older (Dietz et al., 2020) and 2.79% for American children aged 3–17 years reported by 2016 National Survey of Children’s Health in the United States (Xu et al., 2019). Although the global prevalence of ASC is commonly established at around 1% of the population, population study and cohort studies usually reported a higher estimated prevalence than administrative record studies, which indicates that many individuals with ASC are unrecognized by healthcare services (Underwood et al., 2022).

The (suspected and diagnosed) ASC group in the current study had more difficulties in social skills, routine and switching compared to the non-ASC group. These findings correspond with amassing evidence that social skill deficits are prominent among autistic adults (Lundqvist & Lindner, 2017; Sasson et al., 2013; Tonge et al., 2016). Besides that, the finding also resonates with past findings which disclosed a high extent of construct homogeneity among social skills, routine and switching (Hoekstra et al., 2011; Lundqvist & Lindner, 2017), in which these three sub-scales and also the sub-scale of imagination were considered as social traits in contrast to non-social traits (i.e. factor numbers and patterns) of ASC. However, the current ASC group did not show more difficulties in the imagination and number and pattern factors. The findings echoed Lundqvist and Lindner’s (2017) findings that the AQ sum score (social and non-social traits combined) did not perform better than social traits in AQ.

Furthermore, our findings provided evidence that university students with high autistic traits experienced negative psychosocial outcomes, which might have adverse effects on their psychosocial well-being. From the four studied psychosocial domains, reduced self-efficacy and increased perceived stress were clear psychosocial issues faced by the ASC group. Possibly, these domains are related to executive functioning difficulties experienced by autistic university students. The used stress scale (Cohen et al., 1983) reflects stressful conditions induced by reduced abilities in problem-solving, handling changes and lack of control of the situation, which could be related to poor executive functioning skills, which are of utmost importance for studying at the university.

Out of the five AQ factors (Hoekstra et al., 2011), social skills, routine and attention switching were stronger related to psychosocial outcomes than imagination and numbers and patterns. Moreover, all but the numbers and patterns factor were negatively related to psychosocial outcomes. This aligns with past research which described attention to detail as a cognitive advantage of individuals with ASC (Baron-Cohen et al., 2001; Hughes et al., 2018). In this study, the factor numbers and patterns was indeed positively associated with self-efficacy, emotion regulation and general QoL. From the findings, it seems that at least for the university students with ASC, a focus on numbers and patterns could enhance their functionality and participation, especially in the Asian cultural context which values attention to detail in academic performance (Ward et al., 2021), and focus on maths an numbers in general.

Difficulties in social skills were found to negatively predict all psychosocial outcomes, except for emotion regulation. This indicates that social skills are essential in university life and lacking social skills might lead to poorer psychosocial outcomes. The absence of association between high autistic traits in social skills and emotion regulation might be due to the fact that expressing emotions to others is not a common practice in the Asian culture. The emotion regulation scale used in this study might contain a cultural bias as 40% of the items were about expressing personal feelings to others. This is not common in an Asian collectivist contexts, where maintaining harmonious interdependence is important and where individuals are expected to control personal emotions to promote harmony (King & Chen, 2019). This may also explain why autistic traits accounted for relatively less variance in emotion regulation compared to other identified psychosocial outcomes in this study.

An unexpected finding was that close to 20% of the students reported did not know or preferred not to say whether they had ASC. This implies that one in five university students in the studied sample has a limited understanding and acceptance of ASC. It seems that indeed the knowledge about and acceptance of ASCs is lower in Malaysia than in the United Kingdom (de Vries et al., 2020). This finding also indicates that there is more stigma on ASC among Malaysian university students, which is in line with the reported disparity of ASC stigma in Asian and non-Asian cultures (Someki et al., 2018; Yu et al., 2020). For example, 38% of adult citizens in China show a stigma towards ASC, compared to 14% in the United States. (Yu et al., 2020). ASC stigma in a community is typically due to poor understanding and negative beliefs about ASC, which might cause individuals with high autistic traits to face prejudice and discrimination (Someki et al., 2018; Turnock et al., 2022), and hence discourage them from getting a proper diagnosis or disclose their diagnosis (Thompson-Hodgetts et al., 2020).

In sum, the findings illustrate that having high autistic traits can have deteriorating effects on the QoL of university students. Lacking optimal social skills and experiencing attention shifting difficulties significantly affect the QoL. Similar to previous findings (Baron-Cohen et al., 2001; Hughes et al., 2018), a focus on and excellence in factor numbers and patterns can be considered a strength to have a better QoL. However, having difficulties in social skills (such as making friends and meeting new people), routine (new routine, facing interruption), switching (keeping track of conversations, multi-tasking) and imagination (working out people’s intentions, making up cover stories) was found to be closely associated with reduced QoL. This conclusion should be limited to the Asian academic context only.

Although this study provides an important contribution to our understanding of the ASC and the assessment of autistic traits in university students with and without ASC, there are a number of limitations that warrant discussion. First, the survey was conducted during the Covid-19 pandemic, and findings of stress could be biased due to the effect of the pandemic. Research had suggested that during the tumultuous pandemic period, university students faced higher levels of stress (Debowska et al., 2022; Quintiliani et al., 2022). Second, the groups were not balanced for gender (71.1% women, 26.8% men). Future research should aim to address this issue, particularly because ASC is thought to be more prominent among males. Third, there is a marked imbalance group distribution as the ASC group is only 76, while the non-ASC group is 2130. However, to compensate for groups with unequal variances, a Welch U test was used for the between-group analysis (Fagerland & Sandvik, 2009). Fourth, the current study only involved university students from four higher education institutions in Malaysia; hence, this cannot be generalized to other populations. Finally, our findings cannot be generalized to autistic samples since the current analyses were largely based on students who suspected that they might have an ASC, but who did not have a formal diagnosis.

Future directions

It is important to study in depth why university students who suspect they might have an ASC did not obtain a formal diagnosis, university students who do not disclose their ASC identities or who do not have a formal diagnosis, will not be able to obtain ASC-specific support from the universities (Cage et al., 2020). Without the required support, they might be more prone to have limited or superficial friendships, experience loneliness and mental health issues, and have a poorer academic outcome to the point that some might eventually drop out of university (Cage et al., 2020). Moreover, because difficulties in social behaviour are associated with negative psychosocial outcomes, it is important to study how to improve these social challenges, for example with social skills training (Jobe & White, 2007). Providing a suitable social network and support has been known to be critical for the academic success and study completion for university students with high autistic traits (Accardo et al., 2019).

Conclusion

Data from 2732 students revealed that 0.3% of the students reported having an ASC diagnosis, while 2.5% reported that they suspected having an ASC, but did not obtain a formal diagnosis. This indicates that an estimated 2.8% of students might have a diagnosed or suspected ASC. These two categories of students were found to have poorer self-efficacy, higher perceived stress and poorer QoL, as compared to their peers. However, these students were not significantly different from their peers in emotion regulation. Overall, the findings pointed to negative psychosocial outcomes associated with ASC and culture-specific association between autistic traits and psychosocial outcomes.

Footnotes

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project is supported by a research funding from Wings Melaka [304/PGURU/6501157/T135].

ORCID iDs

Hui Min Low

Mohd Syazwan Zainal

Marieke de Vries

References

Accardo

A. L.

Kuder

S. J.

Woodruff

(2019). Accommodations and support services preferred by college students with autism spectrum disorder. Autism, 23(3), 574–583. https://doi.org/10.1177/1362361318760490

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders (5th ed.). American Psychiatric Publishing.

Anderson

(2020). Schooling for pupils with autism spectrum disorder: Parents’ perspectives. Journal of Autism and Developmental Disorders, 50(12), 4356–4366. https://doi.org/10.1007/s10803-020-04496-2

Au-Yeung

S. K.

Bradley

Robertson

A. E.

Shaw

Baron-Cohen

Cassidy

(2019). Experience of mental health diagnosis and perceived misdiagnosis in autistic, possibly autistic and non-autistic adults. Autism, 23(6), 1508–1518. https://doi.org/10.1177/1362361318818167

Bagherzadeh

Loewe

Mouawad

Batista-Foguet

Araya-Castillo

Thieme

(2018). Spanish version of the Satisfaction With Life Scale: Validation and factorial invariance analysis in Chile. The Spanish Journal of Psychology, 21, E2. https://doi.org/10.1017/sjp.2018.2

Bakker

Krabbendam

Bhulai

Begeer

(2019). Background and enrollment characteristics of students with autism in higher education. Research in Autism Spectrum Disorders, 67, Article 101424. https://doi.org/10.1016/j.rasd.2019.101424

Bargiela

Steward

Mandy

(2016). The experiences of late-diagnosed women with autism spectrum conditions: An investigation of the female autism phenotype. Journal of Autism and Developmental Disorders, 46(10), 3281–3294. https://doi.org/10.1007/s10803-016-2872-8

Baron-Cohen

Wheelwright

Skinner

Martin

Clubley

(2001). The Autism-Spectrum Quotient (AQ): Evidence from asperger syndrome/high-functioning autism, malesand females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 5–17. https://doi.org/10.1023/A:1005653411471

Brugha

T. S.

McManus

Bankart

Scott

Purdon

Smith

Meltzer

(2011). Epidemiology of autism spectrum disorders in adults in the community in England. Archives of General Psychiatry, 68(5), 459–465. https://doi:10.1001/archgenpsychiatry.2011.38

10.

Cage

De Andres

Mahoney

(2020). Understanding the factors that affect university completion for autistic people. Research in Autism Spectrum Disorders, 72, Article 101519. https://doi.org/10.1016/j.rasd.2020.101519

11.

Cage

Howes

(2020). Dropping out and moving on: A qualitative study of autistic people’s experiences of university. Autism, 24(7), 1664–1675. https://doi.org/10.1177/1362361320918750

12.

Cai

R. Y.

Richdale

A. L.

(2016). Educational experiences and needs of higher education students with autism spectrum disorder. Journal of Autism and Developmental Disorders, 46(1), 31–41. https://doi.org/10.1007/s10803-015-2535-1

13.

Chee

Z. J.

de Vries

(2022). Language matters: The Autism-Spectrum Quotient in English, Mandarin and Bahasa Malaysia. Journal of Autism and Developmental Disorders, 52(9), 3814–3824. https://doi.org/10.1007/s10803-021-05253-9

14.

Chiarotti

Venerosi

(2020). Epidemiology of autism spectrum disorders: A review of worldwide prevalence estimates since 2014. Brain Sciences, 10(5), Article 274. https://doi.org/10.3390/brainsci10050274

15.

Cohen

Kamarck

Mermelstein

(1983). A global measure of perceived stress. Journal of Health and Social Behavior, 24, 386–339.

16.

Debowska

Horeczy

Boduszek

Dolinski

(2022). A repeated cross-sectional survey assessing university students’ stress, depression, anxiety, and suicidality in the early stages of the COVID-19 pandemic in Poland. Psychological Medicine, 52(15), 3744–3747. https://doi.org/10.1017/S003329172000392X

17.

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(4), 1281–1294. https://doi.org/10.1007/s10803-019-04343-z

18.

Diener

E. D.

Emmons

R. A.

Larsen

R. J.

Griffin

(1985). The Satisfaction With Life Scale. Journal of Personality Assessment, 49(1), 71–75. https://doi.org/10.1207/s15327752jpa4901_13

19.

Dietz

P. M.

Rose

C. E.

McArthur

Maenner

(2020). National and state estimates of adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 50(12), 4258–4266. https://doi.org/10.1007/s10803-020-04494-4

20.

Dijkhuis

de Sonneville

Ziermans

Staal

Swaab

(2020). Autism symptoms, executive functioning and academic progress in higher education students. Journal of Autism and Developmental Disorders, 50(4), 1353–1363. https://doi.org/10.1007/s10803-019-04267-8

21.

Doi

Kobayashi

Takebayashi

Mizokawa

Nakagawa

Mimura

Horikoshi

(2021). Associations of autism traits with obsessive compulsive symptoms and well-being in patients with obsessive compulsive disorder: A cross-sectional study. Frontiers in Psychology, 12, Article 697717. https://doi.org/10.3389/fpsyg.2021.697717

22.

Fagerland

M. W.

Sandvik

(2009). Performance of five two-sample location tests for skewed distributions with unequal variances. Contemporary Clinical Trials, 30(5), 490–496. https://doi.org/10.1016/j.cct.2009.06.007

23.

Farsinejad

Russell

Butler

(2022). Autism disclosure – The decisions autistic adults make. Research in Autism Spectrum Disorders, 93, Article 101936. https://doi.org/10.1016/j.rasd.2022.101936

24.

Fletcher-Watson

Leekam

S. R.

Findlay

J. M.

(2013). Social interest in high-functioning adults with autism spectrum disorders. Focus on Autism and Other Developmental Disabilities, 28(4), 222–229. https://doi.org/10.1177/1088357613480829

25.

Freeth

Sheppard

Ramachandran

Milne

(2013). A cross-cultural comparison of autistic traits in the UK, India and Malaysia. Journal of Autism and Developmental Disorders, 43(11), 2569–2583. https://doi.org/10.1007/s10803-013-1808-9

26.

Gross

J. J.

John

O. P.

(2003). Individual differences in two emotion regulation processes: Implications for affect, relationships, and well-being. Journal of Personality and Social Psychology, 85, 348–362. https://doi:10.1037/0022-3514.85.2.348

27.

Gurbuz

Hanley

Riby

D. M.

(2019). University students with autism: The social and academic experiences of university in the UK. Journal of Autism and Developmental Disorders, 49(2), 617–631. https://doi.org/10.1007/s10803-018-3741-4

28.

Harvery

Froude

E. H.

Foley

K. R.

Trollor

J. N.

Arnold

S. R.

(2021). Employment profiles of autistic adults in Australia. Autism Research, 14(10), 2061–2077. https://doi.org/10.1002/aur.2588

29.

Hoekstra

R. A.

Vinkhuyzen

A. A.

Wheelwright

Bartels

Boomsma

D. I.

Baron-Cohen

Posthuma

Van Der Sluis

(2011). The construction and validation of an abridged version of the Autism-Spectrum Quotient (AQ-Short). Journal of Autism and Developmental Disorders, 41(5), 589–596. https://doi.org/10.1007/s10803-010-1073-0

30.

Hossain

M. M.

Khan

Sultana

McKyer

E. L. J.

Ahmed

H. U.

Purohit

(2020). Prevalence of comorbid psychiatric disorders among people with autism spectrum disorder: An umbrella review of systematic reviews and meta-analyses. Psychiatry Research, 287, Article 112922. https://doi.org/10.1016/j.psychres.2020.112922

31.

Huang

Arnold

S. R.

Foley

K. R.

Trollor

J. N.

(2020). Diagnosis of autism in adulthood: A scoping review. Autism, 24(6), 1311–1327. https://doi.org/10.1177/1362361320903128

32.

Hughes

J. E.

Ward

Gruffydd

Baron-Cohen

Smith

Allison

Simner

(2018). Savant syndrome has a distinct psychological profile in autism. Molecular Autism, 9(1), 1–18. https://doi.org/10.1186/s13229-018-0237-1

33.

Ilias

Cornish

Kummar

A. S.

Park

M. S. A.

Golden

K. J.

(2018). Parenting stress and resilience in parents of children with autism spectrum disorder (ASD) in Southeast Asia: A systematic review. Frontiers in Psychology, 9, 1 –14. https://doi.org/10.3389/fpsyg.2018.00280

34.

Jansen

Petry

Ceulemans

Noens

Baeyens

(2017). Functioning and participation problems of students with ASD in higher education: Which reasonable accommodations are effective? European Journal of Special Needs Education, 32(1), 71–88. https://doi.org/10.1080/08856257.2016.1254962

35.

Jerusalem

Schwarzer

(1995). General Self-Efficacy Scale: Revised – English version. https://psycnet.apa.org/doiLanding?doi=10.1037%2Ft18916-000

36.

Jobe

L. E.

White

S. W.

(2007). Loneliness, social relationships, and a broader autism phenotype in college students. Personality and Individual Differences, 42(8), 1479–1489. https://doi.org/10.1016/j.paid.2006.10.021

37.

King

R. B.

Chen

(2019). Emotions in education: Asian insights on the role of emotions in learning and teaching. The Asia-Pacific Education Researcher, 28(4), 279–281. https://doi.org/10.1007/s40299-019-00469-x

38.

Kjell

O. N.

Diener

(2021). Abbreviated three-item versions of the satisfaction with life scale and the harmony in life scale yield as strong psychometric properties as the original scales. Journal of Personality Assessment, 103(2), 183–194. https://doi.org/10.1080/00223891.2020.1737093

39.

Kurita

Koyama

Osada

(2005). Autism-Spectrum Quotient-Japanese version and its short forms for screening normally intelligent persons with pervasive developmental disorders. Psychiatry and Clinical Neurosciences, 59(4), 490–496. https://doi.org/10.1111/j.1440-1819.2005.01403.x

40.

Lai

M. C.

Baron-Cohen

(2015). Identifying the lost generation of adults with autism spectrum conditions. The Lancet Psychiatry, 2(11), 1013–1027. https://doi.org/10.1016/S2215-0366(15)00277-1

41.

Lazić

Jovanović

Gavrilov-Jerković

(2021). The General Self-Efficacy Scale: New evidence of structural validity, measurement invariance, and predictive properties in relationship to subjective well-being in Serbian samples. Current Psychology, 40(2), 699–710. https://doi.org/10.1007/s12144-018-9992-6

42.

Lei

Russell

(2021). Understanding the role of self-determination in shaping university experiences for autistic and typically developing students in the United Kingdom. Autism, 25(5), 1262–1278. https://doi.org/10.1177/1362361320984897

43.

Liu

Zhao

Dai

Wang

(2020). Factor structure of the 10-item perceived stress scale and measurement invariance across genders among Chinese adolescents. Frontiers in Psychology, 11, Article 537. https://doi.org/10.3389/fpsyg.2020.00537

44.

Lugo-Marin

Magan-Maganto

Rivero-Santana

Cuellar-Pompa

Alviani

Jenaro-Rio

Canal-Bedia

(2019). Prevalence of psychiatric disorders in adults with autism spectrum disorder: A systematic review and meta-analysis. Research in Autism Spectrum Disorders, 59, 22–33. https://doi.org/10.1016/j.rasd.2018.12.004

45.

Lundqvist

L. O.

Lindner

(2017). Is the Autism-Spectrum Quotient a valid measure of traits associated with the autism spectrum? A Rasch validation in adults with and without autism spectrum disorders. Journal of Autism and Developmental Disorders, 47(7), 2080–2091. https://doi.org/10.1007/s10803-017-3128-y

46.

Maenner

M. J.

Shaw

K. A.

Bakian

A. V.

Bilder

D. A.

Durkin

M. S.

Esler

Furnier

S. M.

Hallas

Hall-Lande

Hudson

Hughes

M. M.

Patrick

Pierce

Poynter

J. N.

Salinas

Shenouda

Vehorn

Warren

Constantino

J. N.

Cogswell

M. E.

(2021). Prevalence and characteristics of autism spectrum disorder among children aged 8 years –autism and developmental disabilities monitoring network, 11 sites, United States, 2018. MMWR Surveillance Summaries, 70(11), 1–16. https://doi.org/10.15585/mmwr.ss7011a1

47.

Maiano

Normand

C. L.

Salvas

M. C.

Moullec

Aimé

(2016). Prevalence of school bullying among youth with autism spectrum disorders: A systematic review and meta-analysis. Autism Research, 9(6), 601–615. https://doi.org/10.1002/aur.1568

48.

Mandy

Clarke

McKenner

Strydom

Crabtree

Lai

M. C.

Skuse

(2018). Assessing autism in adults: An evaluation of the developmental, dimensional and diagnostic interview – Adult version (3Di-adult). Journal of Autism and Developmental Disorders, 48(2), 549–560. https://doi.org/10.1007/s10803-017-3321-z

49.

McConachie

Mason

Parr

J. R.

Garland

Wilson

Rodgers

(2018). Enhancing the validity of a quality of life measure for autistic people. Journal of Autism and Developmental Disorders, 48(5), 1596–1611. https://doi.org/10.1007/s10803-017-3402-z

50.

Mostert-Kerckhoffs

M. A.

Staal

W. G.

Houben

R. H.

de Jonge

M. V.

(2015). Stop and change: Inhibition and flexibility skills are related to repetitive behavior in children and young adults with autism spectrum disorders. Journal of Autism and Developmental Disorders, 45(10), 3148–3158. https://doi.org/10.1007/s10803-015-2473-y

51.

Murray

A. L.

Booth

McKenzie

Kuenssberg

(2016). What range of trait levels can the Autism-Spectrum Quotient (AQ) measure reliably? An item response theory analysis. Psychological Assessment, 28(6), 673–683. https://doi.org/10.1037/pas0000215

52.

Oomen

Nijhof

A. D.

Wiersema

J. R.

(2021). The psychological impact of the COVID-19 pandemic on adults with autism: A survey study across three countries. Molecular Autism, 12(1), 1–21. https://doi.org/10.1186/s13229-021-00424-y

53.

Park

S. H.

Song

Y. J. C.

Demetriou

E. A.

Pepper

K. L.

Norton

Thomas

E. E.

Hickie

I. B.

Hermens

D. F.

Glozier

Guastella

A. J.

(2019). Disability, functioning, and quality of life among treatment-seeking young autistic adults and its relation to depression, anxiety, and stress. Autism, 23(7), 1675–1686. https://doi.org/10.1177/1362361318823925

54.

Preece

D. A.

Becerra

Hasking

McEvoy

P. M.

Boyes

Sauer-Zavala

Chen

Gross

J. J.

(2021). The Emotion Regulation Questionnaire: Psychometric properties and relations with affective symptoms in a United States general community sample. Journal of Affective Disorders, 284, 27–30. https://doi.org/10.1016/j.jad.2021.01.071

55.

Preece

D. A.

Becerra

Robinson

Gross

J. J.

(2020). The Emotion Regulation Questionnaire: Psychometric properties in general community samples. Journal of Personality Assessment, 102(3), 348–356. https://doi.org/10.1080/00223891.2018.1564319

56.

Quintiliani

Sisto

Vicinanza

Curcio

Tambone

(2022). Resilience and psychological impact on Italian university students during COVID-19 pandemic: Distance learning and health. Psychology, Health & Medicine, 27(1), 69–80. https://doi.org/10.1080/13548506.2021.1891266

57.

Robison

J. E.

(2019). Talking about autism – Thoughts for researchers. Autism Research, 12(7), 1004–1006. https://doi.org/10.1002/aur.2119

58.

Rosen

N. E.

Lord

Volkmar

F. R.

(2021). The diagnosis of autism: From Kanner to DSM-III to DSM-5 and beyond. Journal of Autism and Developmental Disorders, 51, 4253 –4270. https://doi.org/10.1007/s10803-021-04904-1

59.

Rosen

T. E.

Mazefsky

C. A.

Vasa

R. A.

Lerner

M. D.

(2018). Co-occurring psychiatric conditions in autism spectrum disorder. International Review of Psychiatry, 30(1), 40–61. https://doi.org/10.1080/09540261.2018.1450229

60.

Salari

Rasoulpoor

Rasoulpoor Shohaimi

Jafarpour

Abdoli

Khaledi-Paveh

Mohammdi

(2022). The global prevalence of autism spectrum disorder: A comprehensive systematic review and meta-analysis. Italian Journal of Pediatrics, 48(1), Article 112. https://doi.org/10.1186/s13052-022-01310-w

61.

Sampson

Upthegrove

Abu-Akel

Haque

Wood

Reniers

(2021). Co-occurrence of autistic and psychotic traits: Implications for depression, self-harm and suicidality. Psychological Medicine, 51(8), 1364–1372. https://doi:10.1017/S0033291720000124

62.

Sasson

N. J.

Nowlin

R. B.

Pinkham

A. E.

(2013). Social cognition, social skill, and the broad autism phenotype. Autism, 17(6), 655–667. https://doi.org/10.1177/1362361312455704

63.

Siebes

Muntjewerff

J. W.

Staal

(2018). Differences of symptom distribution across adult age in high functioning individuals on the autism spectrum using subscales of the autism spectrum quotient. Journal of Autism and Developmental Disorders, 48(11), 3939–3944. https://doi.org/10.1007/s10803-018-3657-z

64.

Smith

Sulek

Abdullahi

Green

C. C.

Bent

C. A.

Dissanayake

Hudry

(2021). Comparison of mental health, well-being and parenting sense of competency among Australian and South-East Asian parents of autistic children accessing early intervention in Australia. Autism, 25(6), 1784–1796. https://doi.org/10.1177/13623613211010006

65.

Solomon

(2020). Autism and employment: Implications for employers and adults with ASD. Journal of Autism and Developmental Disorders, 50(11), 4209–4217. https://doi.org/10.1007/s10803-020-04537-w

66.

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

67.

Sosnowy

Silverman

Shattuck

(2018). Parents’ and young adults’ perspectives on transition outcomes for young adults with autism. Autism, 22(1), 29–39. https://doi.org/10.1177/1362361317699585

68.

Sosnowy

Silverman

Shattuck

Garfield

(2019). Setbacks and successes: How young adults on the autism spectrum seek friendship. Autism in Adulthood, 1(1), 44–51. https://doi.org/10.1089/aut.2018.0009

69.

Sterling

Dawson

Estes

Greenson

(2008). Characteristics associated with presence of depressive symptoms in adults with autism spectrum disorder. Journal of Autism and Developmental Disorders, 38(6), 1011–1018. https://doi.org/10.1007/s10803-007-0477-y

70.

Stevenson

J. L.

Hart

K. R.

(2017). Psychometric properties of the autism-spectrum quotient for assessing low and high levels of autistic traits in college students. Journal of Autism and Developmental Disorders, 47(6), 1838–1853. https://doi.org/10.1007/s10803-017-3109-1

71.

Thompson

Bölte

Falkmer

Girdler

(2019). Viewpoints on how students with autism can best navigate university. Scandinavian Journal of Occupational Therapy, 26(4), 294–305. https://doi.org/10.1080/11038128.2018.1495761

72.

Thompson-Hodgetts

Labonte

Mazumder

Phelan

(2020). Helpful or harmful? A scoping review of perceptions and outcomes of autism diagnostic disclosure to others. Research in Autism Spectrum Disorders, 77, Article 101598. https://doi.org/10.1016/j.rasd.2020.101598

73.

Tonge

N. A.

Rodebaugh

T. L.

Fernandez

K. C.

Lim

M. H.

(2016). Self-reported social skills impairment explains elevated autistic traits in individuals with generalized social anxiety disorder. Journal of Anxiety Disorders, 38, 31–36. https://doi.org/10.1016/j.janxdis.2015.12.005

74.

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

75.

Underwood

J. F.

DelPozo-Banos

Frizzati

John

Hall

(2022). Evidence of increasing recorded diagnosis of autism spectrum disorders in Wales, UK: An e-cohort study. Autism, 26(6), 1499–1508. https://doi.org/10.1177/136236132110596

76.

van Rentergem

J. A. A.

Lever

A. G.

Geurts

H. M

. (2019). Negatively phrased items of the Autism Spectrum Quotient function differently for groups with and without autism. Autism, 23(7), 1752–1764. https://doi.org/10.1177/1362361319828361

77.

Ward

Ren

Qiu

(2023). Autistic traits in the neurotypical Chinese population: A Chinese version of Glasgow Sensory Questionnaire and a cross-cultural difference in attention-to-detail. Journal of Autism and Developmental Disorders, 53(2), 669–676. https://doi.org/10.1007/s10803-020-04829-1

78.

World Health Organization. (2021). Autism spectrum disorders. https://www.who.int/news-room/fact-sheets/detail/autism-spectrum-disorders#:~:text=Epidemiology,prevalence%20varies%20substantially%20across%20studies

79.

Strathearn

Liu

O’Brien

Kopelman

T. G.

Zhu

Snetselaar

L. G.

Bao

(2019). Prevalence and treatment patterns of autism spectrum disorder in the United States, 2016. JAMA Pediatrics, 173(2), 153–159. https://doi.org/10.1001/jamapediatrics.2018.4208

80.

Stronach

Harrison

A. J.

(2020). Public knowledge and stigma of autism spectrum disorder: Comparing China with the United States. Autism, 24(6), 1531–1545. https://doi.org/10.1177/1362361319900839

Self-reported autistic traits and psychosocial outcomes among university students in Malaysia

Abstract

Lay abstract

Keywords

Prevalence and psychosocial outcomes of autism spectrum condition among university students in Malaysia

ASC in higher education context

Self-reported ASC traits and psychosocial outcomes

Methodology

Procedure

Community involvement

Participants

Research instruments

AQ-28

Emotion Regulation Questionnaire

Perceived Stress Scale

The General Self-Efficacy Scale

The Satisfaction with Life Scale

The Autism-Specific QoL

Results

Data analysis

Self-reported ASC rate

Autistic traits

Comparison of emotion regulation, perceived stress, self-efficacy and QoL

Predictors of psychosocial outcomes

Discussion

Future directions

Conclusion

Footnotes

Funding

ORCID iDs

References