Barriers to Mental Healthcare for Autistic and Non-Autistic Adults: An Investigation of Number,Severity and Type of Barriers Encountered

Abstract

Many autistic people experience poor mental health but often face barriers to accessing appropriate care. This study examined whether autistic adults encounter more or different barriers to mental healthcare compared to non-autistic adults. Participants (non-autistic n = 173; diagnosed autistic n = 173; self-identified autistic n = 158) completed an online survey assessing 29 barriers adapted from the Barriers to Healthcare Checklist, rated by presence and severity. Barriers were categorised as person, provider or system related. Analyses of variance compared groups on the mean number and severity of barriers, and chi-square tests compared group incidence of each barrier. Overall, systemic barriers (e.g. cost) and difficulties with system navigation (e.g. finding services) were the most commonly reported barriers and rated as the most severe. Autistic participants experienced a significantly higher number and severity of barriers than non-autistic participants. Provider-related barriers (practitioners’ knowledge, attitudes and skills) were particularly severe for autistic groups. In contrast, person-level barriers (e.g. sensory sensitivities), though often reported in general healthcare, were less impactful in mental healthcare. Findings suggest that efforts to improve mental healthcare access for autistic people should prioritise addressing the most frequent and severe barriers: system and navigation challenges, and practitioner knowledge and skills in autism.

Lay abstract

Autistic people often experience poor mental health but face many challenges when trying to access mental healthcare. These challenges can include things like high costs, difficulty finding the right support or healthcare professionals not understanding autism. We looked at whether autistic adults face more or different barriers to mental healthcare compared to non-autistic adults. We surveyed over 500 adults, including those with a formal autism diagnosis, those who self-identified as autistic, and non-autistic adults. The survey asked about 29 possible barriers to getting mental healthcare. These included things related to the person (such as anxiety, sensory sensitivities or difficulty finding services), the healthcare provider (like psychologists’ and psychiatrists’ lack of autism knowledge, or poor communication) and the healthcare system (such as long waiting lists or high cost). We asked whether participants had experienced each barrier and how much each one affected their ability to get help. We found that nearly everyone experienced some barriers, especially challenges with the healthcare system and knowing how to find help, but autistic participants faced more barriers and were more strongly affected by many of them than non-autistic participants. Compared to non-autistic people, autistic people had a lot of problems with providers which prevented them getting the care they needed. We concluded that, to improve access to mental health support for autistic adults, we need to focus on the most common and harmful barriers: making the system easier to use, reducing costs and improving practitioners’ understanding of autism and working with autistic people. By addressing these areas, we can help ensure that autistic people receive the mental healthcare they need.

Keywords

autism barriers healthcare mental healthcare barriers neurodivergence

Introduction

Autistic people have significantly poorer life and health outcomes than the non-autistic population (Bishop-Fitzpatrick & Kind, 2017; Lawson et al., 2020), with higher rates of many physical health conditions and exceptionally high rates of mental health concerns (Croen et al., 2015; Lai et al., 2019). The lifetime incidence for autistic people experiencing psychiatric conditions is at least 70% (American Psychiatric Association, 2013), and many have multiple conditions and chronically poor mental health (Lai et al., 2019). Despite these high rates, autistic people face many barriers to adequate healthcare (for reviews about general healthcare see Bradshaw et al., 2019; A. J. Doherty et al., 2020; Mason et al., 2019; Walsh et al., 2020), resulting in negative impacts to well-being and quality of life (David et al., 2025).

Research about general healthcare shows autistic adults experience a range of barriers that can be broadly classified as originating from the individual (e.g. challenges with communication, executive functioning, anxiety and sensory sensitivities), providers (e.g. practitioners not understanding autism and the needs of autistic clients) or healthcare systems (e.g. lack of funding and resources; Andersen, 1995; Bradshaw et al., 2019; Walsh et al., 2020). Barriers may be particularly onerous for individuals who have intellectual disability (A. J. Doherty et al., 2020).

Two studies compared barriers to general healthcare for autistic and non-autistic adults in America (Raymaker et al., 2017) and Australia (Arnold et al., 2023) using the Barriers to Healthcare Checklist (BHC; Raymaker et al., 2017). Raymaker et al. found autistic adults experienced both more, and different, healthcare barriers compared to non-autistic adults with and without disabilities (including physical, sensory and cognitive disabilities). Different barriers for autistic participants were primarily person-level factors, including issues associated with anxiety, executive functioning, communication, sensory issues and challenges navigating the healthcare system. Arnold et al. (2023) similarly found that Australian autistic participants reported experiencing more barriers than non-autistic participants which were almost exclusively at the person level, including challenges recognising symptoms of mental health deterioration, tolerating the health service waiting room and doing necessary follow-up after appointments, such as filling prescriptions and making further appointments.

Compared to research on barriers to general healthcare, there are few studies about barriers to mental healthcare for autistic adults, and to date, the BHC has not been adapted to encompass mental healthcare. Existing research specifically focusing on mental healthcare barriers has primarily used qualitative designs, which provide rich descriptions of the difficulties autistic people face when seeking mental healthcare. These studies often reported severe impacts of certain barriers that were less prominent, or not mentioned in general healthcare barriers research, particularly at the system or provider level. Such barriers included being ineligible for mental health services due to a co-occurring autism diagnosis (Camm-Crosbie et al., 2019; Dückert et al., 2023), or practitioners’ poor attitudes or lack of knowledge about autism and the needs of autistic people (Camm-Crosbie et al., 2019; Crane et al., 2019; Dückert et al., 2023), leading to miscommunication, diagnostic overshadowing (inaccurately attributing mental health issues to autism) and inappropriate or even harmful treatment (Brede et al., 2022; Camm-Crosbie et al., 2019; Crane et al., 2019; Dückert et al., 2023; Memon et al., 2016; Mirzaian et al., 2024). These findings suggest that certain barriers identified in general healthcare research are more prominent and impactful for mental healthcare or there may be different barriers to mental healthcare compared to general healthcare.

To the best of our knowledge, no published quantitative studies have specifically focused on barriers to mental healthcare for autistic adults, although some included mental healthcare along with general healthcare or included questions about barriers in their studies on wider mental healthcare issues. For example, Vogan et al. (2017) tracked healthcare service usage for 40 autistic Canadian adults. They included a 13-item checklist on barriers to general and mental healthcare encompassing mainly person-level barriers, plus two system navigation barriers (“steps too overwhelming” and “not knowing where to find help”) and one provider barrier (having a “negative experience”). The barriers experienced most commonly were the two system navigation barriers, the provider barrier and one person barrier: “difficulty describing difficulties and needs.” However, the authors did not distinguish between general and mental healthcare services when reporting their results, making it difficult to know if any barriers are worse or different for mental healthcare access.

Another quantitative study, conducted in Germany (Lipinski et al., 2019), examined factors associated with satisfaction with psychotherapy for 245 matched pairs of autistic and non-autistic adults with a primary diagnosis of depressive disorder, also investigating barriers to obtaining psychotherapy via a brief checklist. Autistic participants unable to commence psychotherapy nominated providers’ lack of experience with autism as the main barrier to obtaining it (43% compared to 3% of non-autistic participants), while non-autistic participants more frequently nominated lengthy waiting lists (82% compared with 40% of autistic participants). Being unable to initiate contact with providers in the preferred manner was also nominated by 27% of autistic participants but only 10% of non-autistic participants. Where they did receive psychotherapy, autistic participants gave significantly lower ratings than non-autistic participants on practitioners’ knowledge about their condition.

These studies were among the first to investigate the number of barriers autistic people encounter when seeking mental healthcare, but they do not provide a comprehensive picture as they considered only a limited range of barriers which generally neither encompassed all barriers identified in qualitative research, nor the three broad barrier types (individual, provider and systemic barriers). In addition, there was no consistency in measures used to assess barriers, and to date, only one quantitative mental healthcare study compared autistic and non-autistic individuals (Lipinski et al., 2019). Thus, it is unknown how frequently the mental healthcare barriers identified in qualitative studies, such as systemic and provider barriers, are experienced across autistic populations, nor how this compares with the experiences of non-autistic individuals.

Moreover, findings about healthcare barriers may be a function of the methodologies chosen. In quantitative studies about barriers to healthcare, yes/no checklists are generally used to indicate whether a barrier has been experienced (e.g. the BHC), with researchers reporting only the incidence of participants experiencing each barrier, and the total number of barriers encountered by individuals, but not their severity (Arnold et al., 2023; Vogan et al., 2017). Within the stress-process model (Pearlin et al., 1981, 2005), barriers function as stressors that vary in both origin and intensity, with multiple stressors accumulating to produce compounding effects on well-being or healthcare access. It is therefore important to consider the severity of barriers in addition to the number of barriers experienced. However, to our knowledge, no research has considered this with regard to autistic people obtaining suitable healthcare. Therefore, it remains unknown which are the most critical barriers to mental healthcare (i.e. frequently experienced and having major impacts on obtaining suitable care) nor how to target strategies to address these barriers.

Our study aimed to address these gaps by using quantitative methods to compare autistic and non-autistic adults’ reports of barriers to mental healthcare in terms of the number of barriers experienced, type of barriers and the severity of their impact on obtaining adequate care. Our research questions were:

Do autistic and non-autistic adults differ on the: (a) total number of barriers they face to obtaining adequate mental healthcare; (b) mean severity rating of barriers; (c) severity of impact of each type of barrier (individual, system or provider); and (d) incidence (i.e. the proportion of each group that has experienced a particular barrier) and severity of each barrier?

Do the number and severity of barriers to mental healthcare differ across age, gender or other demographic factors?

What are the primary reasons autistic and non-autistic adults may not want professional mental healthcare?

Due to the small amount of literature on mental healthcare barriers, our study was primarily exploratory. However, based on the existing literature, including that for general healthcare, we expected to find that autistic participants would report experiencing more barriers to mental healthcare than non-autistic participants and that barriers would span the range of person, practitioner and systemic barriers.

Method

Participants

Eligibility criteria comprised adults residing in Australia who self-reported having experienced mental ill-health in their lifetime. People with an intellectual disability were not excluded, but they needed sufficient English language skills to complete an online survey. Participants were 504 adults, belonging to one of three autism-status groups, based upon self-report at eligibility screening: (1) not identifying as autistic (n = 173, “non-autistic”); (2) having a formal autism diagnosis (n = 173, “autistic-diagnosed”) or (3), self-identifying as autistic (n = 158, “autistic-self-ID”). Most autistic-diagnosed participants reported being diagnosed in adulthood, many within the 2 years before the survey (54.3%). Some participants in each group also had other neurodivergent (ND) profiles, most commonly attention-deficit hyperactivity disorder (ADHD).

Table 1 outlines participants’ demographic profiles. The sample was approximately geographically representative of the Australian population but with over-representation of Victorian residents and under-representation of New South Wales residents. There was good representation of Indigenous Australians (5.4%). With reference to Australian demographics (Australian Bureau of Statistics, 2022a, 2022b), our sample was unrepresentative in terms of age (younger: M = 37.8 years, SD = 13.17, range 18–74 years), gender (predominantly female, and with overrepresentation of people identifying as gender queer, non-binary or another gender), cultural heritage (higher proportions born in Australia or other English-speaking countries) and education (levels above the population average). Few participants (n = 7) reported having an intellectual disability, 44 reported having specific learning difficulties (SLDs; e.g. dyslexia) and 179 reported having an attention condition such as ADHD.

Table 1.

Sociodemographic Profile of Participants.

Demographic variable		N	n	%
Autism status (self-reported)		504
Autistic-diagnosed			173	34.3
Autistic-self-ID			158	31.3
Non-autistic			173	34.3
Autism diagnosis ⩽2 years (diagnosed group only)			94	54.3
Autism diagnosis ⩽5 years (diagnosed group only)			128	73.9
Neurotype^a		504
Non-neurodivergent			131	26.0
ND^b, not autistic			42	8.3
Autistic^c only			147	29.2
Autistic + ND^d			184	36.5
Gender identity		503^e
Male/man			62	12.3
Female/woman			359	71.2
Non-binary/gender queer/non-conforming			65	12.9
Other gender identity (primarily transgender)			17	3.4
State or Territory		504
Australian Capital Territory (ACT)			12	2.4
New South Wales (NSW)			123	24.4
Northern territory (NT)			4	0.8
Queensland (Qld)			94	18.7
South Australia (SA)			46	9.1
Tasmania (Tas)			17	3.4
Victoria (Vic)			163	32.3
Western Australia (WA)			45	8.9
Location		504
City			411	81.5
Non-city			93	18.5
Cultural minority^f		504
Not minority			453	89.9
Minority	(5.4% Indigenous)		51	10.1
Education level (highest level completed)		504
Secondary school or lower			133	26.4
Trade/TAFE			86	17.1
Undergraduate			189	37.5
Post-graduate			96	19.0
Main daily activity		504
Working (not studying)			160	31.7
Studying (could be working)			100	19.8
No main activity/unemployed			128	25.4
Other activity, not working or studying (e.g. caring, retired)			116	23.0

TAFE: Technical and Further Education – generally certificates and diplomas.

Includes only neurodivergent (ND) conditions listed in the survey, that is, ADHD/ADD, Communication disorder, Specific Learning Difficulty, Dyspraxia, Tic disorder or Intellectual disability.

79% of those with an ND reported having ADHD/ADD. All NDs other than autism were grouped together for analysis.

For this variable “Autistic” includes both diagnosed and self-identified individuals.

A co-occurring ND.

One case = “prefer not to say” – treated as missing data.

“Minority” = those who identified as either Indigenous or were born in a non-English-speaking country and/or spoke a language other than English at home, “Not minority” = all other participants. Those who did not wish to state whether they were Indigenous were treated as missing data for this variable.

Groups differed significantly on three factors: age, gender identity and ND profiles other than autism. Mean age was significantly higher for the non-autistic group compared to both autistic groups (non-autistic: M = 41.57, SD = 13.99; autistic-diagnosed: M = 34.06, SD = 11.89; autistic-self-ID: M = 37.73, SD = 12.44. F(2,501) = 14.87, p < .001), with autistic-diagnosed participants also having significantly lower mean age than autistic-self-ID participants. A greater proportion of autistic-self-ID participants, and fewer non-autistic participants, identified as non-binary or gender queer than would be expected by chance (i.e. adjusted standardised residuals were ⩽ or ⩾2; x²(6, 503) = 25.43, p < .001), while fewer autistic-diagnosed and more non-autistic participants identified as female than expected by chance. Finally, more autistic-diagnosed and fewer non-autistic participants than expected reported being ND (other than being autistic: x²(2, 504) = 55.81, p < .001).

Measures

Online Survey

A draft online survey focused on participants’ mental health, and their access to, and experiences of, barriers to obtaining suitable mental healthcare, was drafted by the first author based upon previous research (Camm-Crosbie et al., 2019; Crane et al., 2019; Lipinski et al., 2019; Raymaker et al., 2017). A panel of five autistic consultants then advised on the suitability of the survey, resulting in changes to wording, structure and content, and tested the survey twice following modification. Barrier items developed were based upon the BHC (Raymaker et al., 2017) but adapted to have a mental health focus and to better reflect the Australian context. Most items were adapted by simply adding the word “mental” (e.g. “health” and “healthcare provider” became “mental health” and “mental healthcare provider”). Other items were reworded more substantially (e.g. “I had trouble recognising my mental health symptoms and wasn’t sure I needed help,” rather than “When I experience pain and/or other physical symptoms, I have difficulties identifying them and reporting them to my healthcare provider”). New items were added to reflect barriers to mental healthcare noted in previous literature or suggested by the autistic consultants (e.g. “The provider did not recognise or did not accept my autism”). For participants without a formal autism diagnosis, wording was changed where items referred to autism, for example, “The provider did not recognise or did not accept a condition I believe I have.” The final list consisted of 29 items. A 30th item gave participants the opportunity to describe and rate any other barrier (see Table 2 for item wording and abbreviations used in tables and figures, and Supplementary Table 1 showing which items were adapted from the BHC).

Table 2.

Key to Barrier Item Names Showing Actual Survey Wording, Grouped According to Barrier Type.

Brief item name	Full item wording in survey
Person barriers
Recognising own need	I had trouble recognising my mental health symptoms and wasn’t sure I needed help.
Where help	I didn’t know where to get appropriate help.
How to find	I didn’t know how to find a service or practitioner that was right for me.
Not eligible	I didn’t know if I was eligible for mental health support.
Steps	The steps to getting help were too overwhelming.
Advised not to	Someone (such as a support worker, friend, partner or family member) did not want me to get professional mental healthcare or advised me against it.
Inadequate support	I had inadequate social, family, or caregiver support to attend an appointment.
Comm not understood	My attempts to communicate my problems and needs were not understood well enough to get the right help.
Paperwork hard	I had problems filling out the paperwork (including online forms) involved in making or attending an appointment.
Fear/emotion	Fear, anxiety, embarrassment or frustration stopped me from making or attending an appointment.
Stigma	Concern about being stigmatised or labeled stopped me from making or attending an appointment.
Sensory discomfort	Sensory discomforts (for example the lights, smells or sounds) got in the way of attending an appointment.
Waiting room	I found it too hard to handle the waiting room.
Follow-up	I had trouble following up on care (for example, going to the pharmacy, taking prescribed medication at the right time, or making a follow-up appointment).
Not use info	It was difficult to work out how to put the information provided into practice to improve my mental health.
Provider barriers
Not taken seriously	My needs were not taken seriously enough by a healthcare provider
Poor Comm	The provider or their staff did not communicate with me the way I needed.
Wrong Comm	I could not communicate with providers or make appointments the way I preferred.
Behav’r misunderstood	My behaviour was misunderstood or misinterpreted by healthcare staff.
Not Accommodated	I couldn’t find a mental health provider who could accommodate my needs.
Pract No Knowledge	I couldn’t find a mental health provider who understood autism/my condition.
Didn’t like pract	I didn’t like the mental healthcare provider or how they treated me, or I thought they could not meet my needs.
Aut/Cond not recognised	The provider did not recognise or did not accept my autism/ a condition I believe I have.
Not included	I was not included in discussions with the healthcare provider. For example, the provider only talked to my support person.
System barriers
Refused help	I was refused help.
Long wait	The waiting list/time was too long.
Culture wrong	I couldn’t find a mental health provider I felt culturally safe or comfortable with.
Cost	The cost was too high, or I couldn’t afford it.
Transport/distance	Distance or transport issues made it too hard to get to an appointment.
Other	Other barriers: please state?

Participants rated items on 5-point Likert-type scales (1 = “A very slight barrier,” 5 = “A severe barrier”) to indicate the extent to which each barrier had “ever prevented you from getting good mental healthcare” (barrier “severity rating”). Participants rated items as zero if they had never experienced the barrier. Participants who had never seen a mental health practitioner (n = 19) did not see six items about attending a mental healthcare appointment.

Barrier-Type Classification

Consistent with previous healthcare barriers research (Bradshaw et al., 2019; Walsh et al., 2020) and based upon items’ face validity and how they were classified in the BHC and other research (e.g. Vogan et al., 2017), barrier items were classified for analysis as being primarily associated with either the person (individual participant), the provider (the health practitioner or practice) or the system (factors associated with government laws, policies and service provision or wider society or infrastructure). Some items did not neatly fit within this taxonomy (e.g. the steps to getting help were too overwhelming”), but we attempted to remain as close to original classification as possible. For example, in Vogan et al.’s study (2017), the “steps” barrier was considered an emotional barrier and hence is a person barrier in our taxonomy, even though it could be considered more a system navigation barrier. See Table 2 for a complete list of barrier items in each type.

Barrier Variables

Three variables were created for analysis of the barrier ratings:

Total barriers (%): calculated as the number of barriers an individual participant rated above zero divided by the total number of barriers presented (30 or 24), multiplied by 100, for example, a participant who did not see a mental health provider was presented with 24 barrier items, if they gave a rating above 0 to 20 of these (83% of the barriers presented to them), their Total Barrier score = 83%.

Mean severity rating: calculated by summing the ratings given by individual participants to all items presented, divided by the number of barriers rated above 0.

Barrier incidence (%): proportion of participants in each autism-status group experiencing a particular barrier.

Reasons for Not Wanting Mental Healthcare

Participants who indicated they had never wanted professional mental healthcare were asked to indicate their reasons for this. Eleven options were provided including an “other reason” option, which could be described. Participants could choose multiple options if applicable. Provided options were (1) thought the condition was not severe enough, (2) symptoms improved, (3) preferred to manage it myself, (4) used non-mainstream treatments, (5) thought no-one could help, (6) thought no help was available, (7) afraid of stigmatising or labelling, (8) no belief or trust in mainstream treatment/practitioners, (9) past negative experience and (10) thought the barriers were too great.

Sociodemographic Factors

Participants provided information about their age in years, gender identity, state/territory location, location type (city or non-city), cultural identity, education level, main daily activity, autism-status (autistic-diagnosed, autistic-self-ID or non-autistic) and presence of other non-autism NDs (categorised as “non-neurodivergent”; “ND, not autistic”; “autistic only”; and “autistic + (co-occurring) ND.” Note that “autistic” in this variable includes both autistic-diagnosed and autistic-self-ID participants, and all types of non-autism NDs were grouped together for analysis as there were very few participants with NDs other than ADHD to analyse data by ND type). See Table 1 for participants’ sociodemographic profile and further details about sociodemographic factor subcategories.

Procedure

Following approval by the La Trobe University Human Research Ethics Committee (HEC21128), individuals were invited to participate via either notices advertised by autism or mental health organisations, or paid Facebook advertising. Participants followed an advertisement link, and after reading the study information and providing online consent, completed the survey anonymously online. Data were collected and managed using the REDCap platform (Harris et al., 2019) hosted at La Trobe University.

Participants took an average of 22 minutes to complete the survey until the end of the barriers’ questions although actual completion time varied depending upon breaks taken or extent of written comments; the average completion time was calculated after removing outliers taking more than 1 h (n = 33). These outliers may have left the survey open and had a short break, while others may have closed the survey and returned later using an access code, with some of these participants returning days later. Participants could exit the survey at any time; responses to the last completed section were included in the analyses. Upon survey completion, participants could opt to enter a prize draw for one of 16 AUD$50 shopping vouchers; this was not connected to their survey responses. The survey was open from September 2021 till September 2022.

Data Analysis

Data were cleaned by examining very fast response times (<10 min for the entire survey), inconsistent responding (e.g. their postcode did not match the State or region they said they lived in), odd comments, missing data and other anomalies. Obviously spurious cases were eliminated prior to data analysis (n = 31). Data were analysed quantitatively using IBM SPSS (Version 29). Missing data values were not imputed but excluded pairwise where necessary. This applied only to the barrier item-level analyses as there was a complete dataset for the group-level analyses apart from gender where one case was missing. At the item-level, the sample ranged from N = 255 to 463 depending on how many participants indicated they had not experienced the barrier item.

Analyses of variance (ANOVAs) were conducted to compare autism-status groups on mean total barriers, mean severity ratings, mean severity rating by barrier type and mean item severity rating, and to examine sociodemographic factors (except age) associated with mean total barriers and mean severity ratings. Tukey’s post hoc test was used for significant ANOVA outcomes. Welch ANOVA with Games–Howell post hoc tests for significant outcomes was used for variables that were not normally distributed and the Levene test was significant (Field, 2024). Age was examined using Pearson’s correlation, with the relationship between barrier incidence and severity being estimated by obtaining 95% confidence intervals (CIs) using 1000 bootstrapped samples with bias-corrected and accelerated (BCa) intervals. For ANOVAs, effect size was measured using omega² (w²: fixed effect) with thresholds of <.06 = small, .06–.13 = medium and >.14 = large.

Group differences in the percentage of participants experiencing each barrier (“incidence”) were assessed using chi-square tests. Similarly, proportions of participants in each autism-status group were also compared using chi-square for each category of “other barrier,” as were participants’ reasons for not wanting professional mental healthcare. Adjusted standardised residuals were examined for differences from chance on significant chi-square tests, and effect size was measured using Cramer’s V with <.2 = small, .21–.34 = medium and >.34 = large. Participants’ descriptions of “other barriers” experienced were classified and reported narratively, as were “other reasons” nominated by participants for not wanting mental healthcare. Responses to these “other” items were insufficiently detailed to perform formal thematic analysis. Instead, responses were grouped into broad categories where at least two participants made similar comments.

Mean incidence and severity rating of each barrier item were also mapped onto scatterplots, along with indication of barrier type, to examine the relationship between these variables and visually highlight any group differences. Due to multiple comparisons examining mean severity and incidence at the item level (30 items), we applied Benjamin–Hochberg false discovery rate (FDR) correction (q = .05) across the omnibus ANOVA and chi-square p values, as this correction maintains an appropriate balance between Type I and Type II error by limiting the expected proportion of false positives while preserving statistical power (Chen et al., 2017). Post hoc Tukey tests or standardised residuals were then inspected only for items that remained significant after FDR adjustment.

Results

Barrier Incidence and Severity

Most barrier items were experienced by at least 50% of participants in each autism-status group (“barrier incidence”). The exceptions were “not included” and “other barriers,” and for the non-autistic group “advised not to,” “paperwork too hard” and “sensory discomforts” also had lower incidence (see Supplementary Table 2). There was a linear relationship between barrier incidence and severity rating (r = .81, p < .001, 95% BCa CI: 0.76, 0.84), where higher barrier incidence was strongly associated with higher severity ratings (Supplementary Figure 1).

Comparison of Autism Status Groups

Incidence

After correcting for multiple comparisons, 25 barrier items showed no significant group differences in mean incidence, based on chi-square tests (see Supplementary Table 2). The remaining items (n = 5) were experienced by significantly fewer non-autistic participants and more autistic-diagnosed participants than expected by chance. Adjusted standardised residuals were ⩾2 for the autistic-diagnosed group and ⩽2 for the non-autistic group on: “communication not understood,” “behaviour misunderstood,” “practitioner no knowledge,” “sensory discomfort” and “autism/condition not recognised.” One item (“sensory discomforts”) was also experienced by significantly more autistic-self-ID participants than expected by chance. Effect sizes were small for all but “sensory discomforts” which was in the medium range (V = .27).

Total Barriers, Barrier Severity and Barrier Type Means

Table 3 shows ANOVA and post hoc results for the differences between autism-status groups in mean total barriers, mean severity ratings and mean severity ratings by barrier type. Mean total barriers were significantly higher for autistic-diagnosed participants compared to the non-autistic group but with low effect size. Autistic-diagnosed and autistic-self-ID participants had significantly higher mean severity ratings than non-autistic participants, with effect size approaching the medium range (w² = .05). Regarding the severity of different barrier types (i.e. person, system and provider), system barriers were rated as most severe for all groups, with autistic-diagnosed participants giving significantly higher mean severity ratings than the non-autistic group. For both person and provider barriers, the autistic-diagnosed and autistic-self-ID groups had significantly higher mean severity ratings than non-autistic participants.

Table 3.

Comparison of Autism Status Groups on Mean Total Barriers, Mean Severity Rating for All Barriers and Mean Severity Rating by Barrier Type.

	Autism status group^a
	Autistic-diagnosed (n = 173)		Autistic-self-ID (n = 158)		Non-autistic (n = 173)
	M	SD	M	SD	M	SD	ANOVA
Total barriers^a	75.21%	23.12	72.29%	22.51	67.91%	25.85	F^b(2, 333.13) = 3.82, p = .02 Post hoc^c: *(D > N)
Overall severity rating^a	2.59	1.08	2.42	.97	2.02	0.96	F(2, 501) = 14.68, p < .001Post hoc^d: **(D > N, S > N)
Person barriers	3.24	0.78	3.26	0.76	2.87	0.82	F(2, 492) = 12.54, p < .001Post hoc^d: **(D > N, S > N)
System barriers	3.50	1.02	3.42	1.02	3.18	1.05	F(2, 476) = 4.11, p = .02 Post hoc^d: *(D > N)
Provider barriers	3.44	1.10	3.31	0.91	2.91	1.11	F(2, 476) = 11.21, p < .001Post hoc^d: *(D > N) (S > N)

“Total barriers” score represents the number of barriers experienced by a participant, expressed as a percentage of all barriers presented. “Overall Severity Rating” represents the sum of ratings given to all items presented, divided by the number of barriers rated above zero. D > N = autistic-diagnosed significantly higher than non-autistic. S > N = autistic-self-ID significantly higher than non-autistic.

Includes all barriers rated >0, including “other barriers.”

Welch ANOVA.

Games–Howell test.

Tukey test.

p < .05, **p < .001 on post hoc tests.

Barrier Item Analysis (Barrier Incidence, Severity and Type) by Group

Item-by-item analyses of mean severity ratings and barrier incidence for each autism status group are displayed in Figures 1 –3 and further detail is provided in Supplementary Table 2. These figures show that, on average, autistic-diagnosed and autistic-self-ID participants rated more barriers above 3.5 in severity compared to non-autistic individuals. Based on mean ratings, the most severe and highest incidence barriers for all participants were those associated with the system (“cost,” “long wait”), or the person’s knowledge of and ability to navigate the system (“steps,” “how to find”). For autistic-diagnosed participants and to a lesser extent the autistic-self-ID participants, provider barriers were also prominent.

Figure 1.

Mean Barrier Item Severity and Barrier Incidence for Autistic-Diagnosed Group (N = 173).

Figure 2.

Mean Barrier Item Severity and Barrier Incidence for Autistic-Self-Identified Group (N = 158).

Figure 3.

Mean Barrier Item Severity and Barrier Incidence for Non-Autistic Group (N = 173).

The 12 most severe and high-incidence barriers across the whole sample are further compared for the autism-status groups in Figure 4. After correcting for multiple comparisons, there were no significant group differences in mean severity for “steps,” “long wait,” “cost” and “not accommodated,” which were rated universally severe by each group. The autistic-diagnosed group rated the remaining eight barriers significantly higher than did non-autistic participants, while autistic-self-ID participants also gave significantly higher mean ratings than non-autistic participants on four barriers: “how to find,” “communication not understood,” “practitioner poor communication” and “practitioner no knowledge.” Effect sizes were medium or close to medium (range w² = .05–.08) for “provider no-knowledge,” “behaviour misunderstood” and “where help,” but small for the remaining significantly different items.

Figure 4.

Comparison of Mean Severity by Autism-Status Groups on 12 Most Severe Barriers with Highest Incidence for Whole Sample.

Demographic Factors by Mean Total Barriers and Severity Rating

Table 4 shows the comparison of mean total barriers for the sociodemographic factor subcategories. Significant differences were found for neurotype and gender identity although with low effect size. On average, significantly more barriers were experienced by autistic people with another co-occurring ND compared to non-ND participants (the main non-autism ND was ADHD). Furthermore, those who identified as non-binary or gender queer experienced significantly more barriers, on average, than those identifying as male. The correlation between age and total barriers was non-significant (r = .016, p = .73).

Table 4.

Mean Total Barriers by Sociodemographic Factors.

	Total barriers^a
Factor	n	Mean %	SD %	ANOVA tests
Neurotype				^bF(3, 164.70) = 6.14, p < .001**
Non-neurodivergent	131	65.46**	26.69
ND, not autistic	42	75.60	21.57
Autistic only	147	70.26	24.89
Autistic + ND	184	76.66**	20.70
Gender Identity				^bF(3, 63.96) = 4.01, p = .01*
Male/man	62	67.00*	25.70
Female/woman	359	71.13	24.85
Non-binary/gender queer	65	78.36*	17.64
Other gender identity	17	77.06	17.07
State or territory				F(7, 496) = 1.41, p = .20
ACT	12	64.17	30.29
NSW	123	70.72	24.34
NT	4	80.83	1.67
Qld	94	71.80	23.67
SA	46	77.16	21.10
Tas	17	76.18	19.57
Vic	163	69.23	25.78
WA	45	78.07	19.74
Location				F(1, 502) = .50, p = .48
City	411	72.16	23.55
Non-city	93	70.19	26.24
Cultural minority				F(1, 501) = .17, p = .68
Not minority	453	71.64	24.23
Minority	51	73.12	22.63
Education level				F(3, 500) = .22, p = .88
Secondary school or lower	133	72.98	23.93
Trade/TAFE	86	72.13	24.29
Undergraduate	189	70.78	24.22
Post-graduate	96	71.83	24.00
Main Daily Activity				F(3, 500) = 1.71, p = .16
Working (not studying)	160	70.74	25.23
Studying (could be working)	100	67.90	21.21
No main activity/unemployed	128	74.17	23.98
Other activity, not working or studying (e.g. caring, retired)	116	74.00	24.56

Mean values represent the percentage of barriers experienced. For those who had never seen a practitioner 24 barriers were presented, for those who had seen a practitioner, 30 barriers were presented. Participants checked which barriers they had ever experienced. Totals were divided by the relevant number presented. *p < .05 and **p < .001 in this column indicates where post hoc tests showed a significant difference between groups.

Welch ANOVA reported where group variances were significantly non-homogeneous on Levene’s test and Games–Howell post hoc test reported where relevant.

The mean barrier severity ratings across the sociodemographic factors were examined (Table 5). Significant differences were found for neurotype (medium effect size), gender identity (low effect size), state or territory (low effect size) and main daily activity (low effect size). Significantly higher mean severity ratings were given by autistic people with and without another co-occurring ND, compared to non-ND individuals, including non-autistic participants who had another co-occurring ND. Mean severity ratings did not differ significantly between non-autistic participants with and without ND. Non-binary or gender queer individuals gave significantly higher mean severity ratings than those who identified as female. For main daily activity, participants with no main activity/unemployed had higher mean severity ratings compared to those who were studying. Post hoc tests were not significant for state/territory but lower mean ratings were given by those from the three most populous States (VIC, NSW, QLD) and the ACT. The correlation of age and severity was non-significant (r = −.012, p = .78).

Table 5.

Mean Severity Ratings by Sociodemographic Factors.

Factor	n	Mean rating	SD	ANOVA tests
Neurotype				F(3, 500) = 14.63, p < .001**^a
Non-neurodivergent	131	1.92	0.99
ND, not autistic	42	2.33	0.80
Autistic only	147	2.31	1.06
Autistic + ND	184	2.67	0.98
Gender Identity				F(3, 499) = 3.53, p = .02*
Male/man	62	2.23	1.05
Female/woman	359	2.28*	1.03
Non-binary/gender queer	65	2.67*	0.98
Other gender identity	17	2.69	0.87
State or territory				F(7, 496) = 2.19, p = .03*^b
ACT	12	2.11	1.16
NSW	123	2.22	1.02
NT	4	2.67	0.37
Qld	94	2.35	0.96
SA	46	2.70	1.06
Tas	17	2.52	0.90
Vic	163	2.23	1.08
WA	45	2.67	0.96
Location				F(1, 502) = .02, p = .89
City	411	2.34	1.02
Non-city	93	2.35	1.08
Cultural minority				F(1, 501) = .06, p = .81
Not minority	453	2.34	1.04
Minority	51	2.38	0.99
Education level				F(3, 500) = 1.46, p = .23
Secondary school or lower	133	2.48	1.04
Trade/TAFE^c	86	2.39	1.04
Undergraduate	189	2.29	0.95
Post-graduate	96	2.21	1.03
Main daily activity				F(3, 500) = 3.25, p = .02*
Working (not studying)	160	2.29	1.08
Studying (could be working)	100	2.12*	0.93
No main activity/unemployed	128	2.54*	1.02
Other activity, not working or studying (e.g. caring, retired)	116	2.38	1.03

Barrier severity was rated on a 5-point scale where 1 = “A Slight Barrier” and 5 = “A Severe Barrier.” Mean values were calculated by summing ratings and dividing by the number of barriers experienced by the participant.

Significant post hoc tests for neurotype: Non-neurodivergent and autistic only: p = .006; non-neurodivergent and autistic + ND: p < .001; autistic only and autistic + ND: p = .007. Non-neurodivergent and ND, not autistic, ND not autistic and autistic only/autistic + ND all non-significant.

No significant differences between states/territories were found on post hoc tests.

TAFE = Technical and further education – generally certificates and diplomas.

All other variables: post hoc tests: *p < .05; **p < .001.

Other Barriers

Fifty-one participants nominated another barrier not listed in the 29 barrier items (autistic-diagnosed n = 22, autistic-self-ID n = 15, non-autistic n = 14). There were no significant differences between groups on proportions nominating different types of additional barriers (Χ²(26) = 30.79, p = .24; see Table 6). The most nominated additional barrier was the perceived poor attitudes of practitioners (e.g. “professionals being very judgemental and not supportive”), followed by participants reporting feeling treatment was ineffective (e.g. “most doctors and psychologists tried to use CBT which only helped a little bit”). Remaining barriers were each cited by fewer participants but spanned the range of barrier types (see Table 6 for further detail and see Supplementary Table 3 for further examples of participant comments about other barriers).

Table 6.

“Other Barriers” Summary.

Barrier	Frequency (n)
Person
Felt did not deserve/did not prioritise own needs	3
Retraumatising to repeat story	1
Too unwell to seek help	1
Provider
Poor practitioner attitudes	19
Non-autism ND not recognised/accommodated	4
No service appropriate for autistic people	4
Not LGBTI inclusive/informed	3
Complexity of condition – providers not able to help	2
Past traumatising or negative experience	2
Poor assessment	1
System
National Disability Insurance Scheme issues	1
Confidentiality for minors	1
Other
Ineffective treatment/medication	7
Other barrier	2

Summary of responses to the prompt to describe “any other barrier.” See Supplementary Table 3 for further detail. Frequencies exclude participant comments that fitted within a listed barrier on the checklist. Participants could nominate more than one other barrier.

Reasons for Not Wanting Mental Healthcare

Sixty participants gave reasons for not wanting professional mental healthcare. There were no significant differences between groups in nominations of each option. Most common reasons for not wanting help were thought no-one could help (65%: Χ²(2) = .05, p = .98), thought the condition was not severe enough (58.3%: Χ²(2) = 3.65, p = .16), preferred to manage it myself (58.3%: Χ²(2) = .55, p = .76), afraid of stigmatising or labelling (56.7%: Χ²(2) = .80, p = .67) and past negative experience (50%: Χ²(2) = 1.00, p = .61). Seventeen participants described other reasons for not wanting professional mental healthcare. These included cost; insufficient support to get help; denying or being unaware of one’s own needs due to the nature of mental health problems, particularly psychosis; seeing one’s issues as being associated with relationships rather than mental health; and disliking what treatment would involve, such as hospitalisation or medication.

Discussion

Most participants, regardless of autism status, encountered multiple barriers to obtaining suitable mental healthcare. Nevertheless, autistic participants, particularly those with a formal diagnosis, were more likely as a group to encounter barriers, to experience more barriers and to rate all types of barriers as more severe than non-autistic participants. This corroborates previous research about the experience of more barriers to general healthcare for autistic compared to non-autistic individuals (Arnold et al., 2023; A. J. Doherty et al., 2020; M. Doherty et al., 2022; Raymaker et al., 2017). However, this is the first study to compare autistic and non-autistic groups on the number of barriers to mental healthcare across barrier types, and to examine barrier severity. Our findings are concerning, given the additional need for mental healthcare in the autistic population (Lai et al., 2019), suggesting more must be done to address these barriers to ensure equitable access for this group.

Our results revealed the importance of provider barriers, including communication issues and practitioners’ lack of knowledge or poor attitudes, for autistic individuals’ experience of mental healthcare. In contrast to non-autistic participants, high proportions of autistic participants (particularly those formally diagnosed) reported experiencing provider barriers and rated these as being among the most severe barriers they encountered. Provider barriers also featured as the most cited “other barrier” in participants’ written comments. Previous research about mental healthcare barriers also found provider barriers were encountered frequently by autistic people (Adams & Young, 2021; Brede et al., 2022). We speculate that provider barriers may have been more severe for autistic-diagnosed participants compared to self-identified autistic people due to the stigmatising effect of a diagnostic label; perhaps when less-knowledgeable providers know a person has an autism diagnosis, they are more likely to express biased and stereotyped views.

Our findings about provider barriers emphasise the need for practitioner education and training to optimise mental healthcare for autistic people, especially since previous qualitative research found providers’ behaviour negatively impacted service experience and often resulted in early treatment withdrawal (Camm-Crosbie et al., 2019; Dückert et al., 2023). In Australia and elsewhere, there is growing awareness of this need, and system-wide change is occurring. For example, Australian psychologists are now required to be competent in working with ND individuals and expected to undergo training if they do not meet this competency (Australian Psychological Society, 2025). Increasingly, training programmes are being developed by or with ND health practitioners or consultants and have a neurodiversity affirming focus (e.g. Reframing Autism, 2025).

A strength of our study was our measurement of barrier severity. By incorporating this factor, we found that while most people experience barriers to obtaining adequate care, only certain kinds of barriers were reported to have a significant impact. Some person barriers reported by Raymaker et al. (2017) as being common for autistic people in general healthcare (sensory discomforts, waiting room issues, being advised not to seek care, paperwork challenges and having difficulty with healthcare follow-up) did not feature in our study in terms of the severity of their impact on access to adequate mental healthcare. Our sample experienced these barriers to an extent but not as often as other barriers and, when encountered, they did not present severe impediments to obtaining suitable care for most autistic participants. It may be that different barriers operate when accessing mental healthcare compared to general healthcare, or that certain barriers are more salient in one setting or the other. However, this cannot be known without further research that systematically measures both severity and incidence of barriers across healthcare contexts.

One type of barrier that did have a significant impact was difficulty understanding and navigating the mental healthcare system, including lacking system knowledge, such as how and where to get help, or lacking the capacity to arrange care. These difficulties were reported by most participants but were particularly common and severe for autistic participants. System navigation barriers were also identified by previous researchers (Adams & Young, 2021; Brede et al., 2022) but quantitative mental healthcare studies did not measure severity, and either did not consider system navigation or did not compare autistic and non-autistic groups. Thus, it was previously unknown whether system navigation issues were more challenging for autistic people seeking mental healthcare than for the general population.

Challenges navigating the system seem to result from an interaction between a complex mental health system and the person’s capacity to navigate it; they are not simply the result of individuals’ lack of skills or capacity (or a “person” barrier). For this reason, we believe system navigation barriers should be considered a separate type of barrier in future healthcare barriers research. Conceiving system navigation barriers in this way implies a systemic response is required to mitigate the most challenging barriers for people seeking mental healthcare, whether autistic or non-autistic. Effective strategies might include providing community education about accessing mental healthcare, and the support to do so, such as a central triage and referral service.

Another commonly reported barrier type was systemic barriers, particularly cost and lengthy delays, which were reported by most participants regardless of their autism status. These barriers were also identified previously in several studies about mental healthcare for autistic people (Adams & Young, 2021; Brede et al., 2022). Our participants were mainly reliant on the private mental healthcare system where, in Australia, there are often large gaps between government subsidies and practitioner fees (see Supplementary Material for a description of the Australian healthcare system). Furthermore, access to private practitioners in Australia relies on the knowledge and support of referring primary healthcare practitioners as there is no central, co-ordinating referral service and access to government subsidy for privately provided mental healthcare is only available via primary healthcare provider referral. Delays to accessing healthcare and difficulty affording care were also reported to be exacerbated in Australia during and following the COVID-19 pandemic (Ball et al., 2026) when data collection for this study occurred (2021–2022). All these factors likely contributed to cost, delays and system navigation issues being rated as the most common and severe barriers by all participants.

Apart from autism status, other factors associated with experiencing a greater number and/or severity of barriers in our study included having another ND co-occurring with autism (including ADHD, SLDs and intellectual disability), being gender divergent, being unemployed and living in less populous areas, the latter likely associated with reduced service provision due to low population density (Australian Institute of Health and Welfare, 2024). These factors may be associated with these individuals having greater mental health needs but fewer resources to obtain care, including knowledge of healthcare systems and financial resources. Furthermore, neurodivergence may exacerbate difficulties with system navigation due to associated executive functioning challenges (Demetriou et al., 2018; Willcutt et al., 2005), and providers may lack skills for helping this population (A. J. Doherty et al., 2020; Hamed et al., 2015), as well as gender-divergent individuals (McCann & Sharek, 2016).

Our study extends the research in this field by comprehensively measuring barriers to mental healthcare for autistic and non-autistic adults, investigating both the incidence and severity of impact of a wide range of barriers to mental healthcare, including those associated with individuals, providers and the healthcare system. Furthermore, basing our instrument on the BHC allowed some comparison with previous general healthcare research. However, direct comparisons are constrained by design differences in which previous quantitative studies only investigated barrier numbers. A further strength was the comparison of autistic (both diagnosed and self-identified) and non-autistic samples, enabling us to draw conclusions about the relative impact of different barriers and the types of barriers most important for each group.

We looked at the number and severity of barriers as separate factors; however, the stress process model (Pearlin et al., 1981, 2005) suggests it is likely their combination determines success in obtaining suitable mental healthcare. According to this model, greater exposure to more intense stressors (barriers in our case) leads to worse outcomes, meaning an accumulation of severe barriers is likely to have a compounding effect on overall access. Compared to non-autistic people, autistic individuals experience an accumulation of relatively severe barriers which, according to the stress-process model, may result in even greater lack of access than the number or severity alone suggest. Future research could investigate the combined effect of total number of barriers and severity to understanding how barriers function collectively to limit mental healthcare access and help guide more targeted policy and service improvements.

Limitations of our study were associated with the self-selection of the sample. This resulted in overrepresentation of late-diagnosed autistic individuals and few participants who reported having an intellectual disability. In addition, most participants were from the cultural majority, meaning our results can only be generalised to similar populations. Our sample was predominantly female, but this reflects the higher incidence of mental health conditions and service usage for females in the general population (Australian Bureau of Statistics, 2023). It is also possible that individuals who had experienced more barriers, or more severe barriers, were more motivated to participate than those who had experienced fewer and less-severe barriers. Furthermore, because mean severity scores were calculated by dividing severity ratings by the number of barriers rated above zero, they can appear artificially high when participants report only a few barriers but rate them as severe. This limitation reinforces the need to interpret incidence and severity together, or to develop composite indices that capture both dimensions.

Although we used self-report of autism status, we took steps to further confirm reported status via additional questions about diagnosis or reasons for believing one-self to be autistic. Our findings were similar to previous research in terms of differences between autism-status groups (e.g. Arnold et al., 2023), and our large group samples should have negated the effect of low levels of misattribution. Finally, there is a need to also investigate other barriers to mental healthcare in addition to those examined in this study. Our participants described several “other barriers” such as feeling treatment was ineffective, being too unwell to seek help, fear of being re-traumatised and practitioners not recognising or accommodating NDs such as ADHD; these could be incorporated in future barriers research.

Conclusion

Most people, regardless of autism-status, experience multiple barriers to obtaining mental healthcare but barriers are particularly numerous and impactful for autistic people and those with co-occurring NDs, or who belong to disadvantaged or minority groups, such as gender-divergent individuals. In our study, systemic barriers, such as cost and long waiting times, were the main barrier for all individuals seeking mental healthcare, along with barriers associated with navigating the system. In addition to these, autistic people faced significant barriers associated with practitioners, including their attitudes, knowledge and skills in working with autistic people. The focus of efforts to improve access to optimal mental healthcare should be on barriers that are experienced by most people and which have the most severe impact, particularly systemic barriers. In addition, equitable mental healthcare for autistic people also must address provider barriers.

Supplemental Material

sj-docx-1-aut-10.1177_13623613261433100 – Supplemental material for Barriers to Mental Healthcare for Autistic and Non-Autistic Adults: An Investigation of Number, Severity and Type of Barriers Encountered

Supplemental material, sj-docx-1-aut-10.1177_13623613261433100 for Barriers to Mental Healthcare for Autistic and Non-Autistic Adults: An Investigation of Number, Severity and Type of Barriers Encountered by Robyn C Ball, Amanda L Richdale, Lauren P Lawson and Eric MJ Morris in Autism

Footnotes

Acknowledgements

The authors would like to thank all study participants for their interest, time and input.

ORCID iDs

Robyn C Ball

Amanda L Richdale

Lauren P Lawson

Eric MJ Morris

Ethics Considerations

Approval for this study was granted by the La Trobe University Human Research Ethics Committee (HEC21128).

Consent to participate

Informed consent to participate in the survey was implied by participants commencing the online survey after reading the study outline and informed consent information.

Author Contributions

Robyn C Ball: Conceptualisation; data curation; formal analysis; investigation; methodology; project administration; visualisation; writing – original draft; writing – review & editing.

Amanda L Richdale: Conceptualisation; methodology; supervision; writing – review & editing.

Lauren P Lawson: Conceptualisation; methodology, supervision; writing – review & editing.

Eric MJ Morris: Conceptualisation; methodology; supervision; writing – review & editing.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Declaration of conflicting interests

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

Data Availability Statement

Data is stored securely by La Trobe University. It will be made available after R.B. has completed the PhD associated with this study via application to the corresponding author.

Supplemental Material

Supplemental material for this article is available online.

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