Reports on Disability and Accessibility in Canada
Barriers to accessibility related to Internet use: Findings from the 2022 Canadian Survey on Disability

Abstract

This study uses the 2022 Canadian Survey on Disability to explore the experiences of barriers to accessibility related to Internet use while doing certain online activities among persons with disabilities aged 15 years and over. Descriptive statistics show that 17% of persons with disabilities experience barriers when using the Internet for a number of online activities and that the proportion experiencing such barriers varied by disability-related and sociodemographic characteristics. Disability-related factors associated with higher odds of experiencing barriers included having more severe disabilities, having unmet needs for disability supports and having unmet needs for help with everyday activities. In terms of sociodemographic factors, the odds of experiencing barriers to accessibility related to Internet use were higher among 2SLGBTQ+ persons and lower among women.

Introduction

The purpose of the Accessible Canada Act (ACA), along with its regulations^Note  and related standards,^Note  is to make Canada barrier-free by 2040. The ACA defines a barrier as anything that prevents persons with disabilities from fully participating in society.^Note  The ACA provides a framework for the identification, removal and prevention of barriers to accessibility. It includes the following priority areas: employment; built environment;^Note  information and communication technologies (ICT);^Note  communication, other than ICT; procurement of goods, services and facilities; design and delivery of programs and services; and transportation. Research indicates that barriers to accessibility can manifest in different forms and affect persons with disabilities in a variety of situations, such as limiting their employment opportunities (Grisé et al., 2019), social participation (Sundar et al., 2016; Wee & Lysaght, 2009) and overall quality of life (Forster et al., 2023).

With the ratification of the United Nations Convention on the Rights of Persons with Disabilities,^Note Note  Canada has adopted the social model of disability, which conceptualizes disability as the result of interactions between functional limitations and barriers to accessibility in the environment (Pianosi et al., 2023). Developing a better understanding of barriers to accessibility encountered by persons with disabilities can inform the design of interventions to remove them and ensure that our communities, workplaces and services are fully accessible.

While previous research has highlighted issues related to barriers to accessibility in Canada (Choi, 2021; McDiarmid, 2021), data from the 2022 Canadian Survey on Disability (CSD) provides an opportunity to more closely examine barriers to accessibility encountered by Canadians with disabilities. The 2022 CSD collected information on 27 types of barriers to accessibility experienced by persons with disabilities because of their condition across four domains: public spaces; behaviours, misconceptions or assumptions; communication; and Internet use. This is the fourth and final report in a series of reports (one for each domain) providing further analyses of barriers to accessibility among persons with disabilities aged 15 years and over. The report focuses on barriers encountered by persons with disabilities related to Internet use.

Using the Internet for various online activities has become an essential part of social participation in everyday life. Prior research has documented lower rates of Internet access and utilization among persons with disabilities (Duplaga, 2017; Johansson et al., 2021; Scholz et al., 2017) and in 2022, close to 15% of Canadians with disabilities reported that they did not use the Internet for personal use (Statistics Canada, 2024), although this did vary by age group with younger persons with disabilities being more likely to use the Internet than older persons with disabilities (Textbox 1). The gap in access to digital services has been termed a digital divide, with research indicating that persons with disabilities are among the groups most vulnerable to digital exclusion due to issues related to accessibility of content and availability of required accommodations (Abdelaal & Andrey, 2022; Raihan et al., 2024). The current study provides a quantitative examination of persons with disabilities experiencing barriers when using the Internet for specific online activities.

The main goal of this report is to examine the associations between disability-related and sociodemographic characteristics and the likelihood of experiencing barriers to accessibility related to Internet use. The report starts with an examination of the prevalence of experiencing barriers to accessibility when using the Internet for specific online activities among persons with disabilities because of their condition. Next, it focuses on how the prevalence of experiencing at least one barrier varies based on disability-related factors and across sociodemographic groups. Finally, logistic regression modelling is used to determine the association of each variable with the odds of experiencing barriers related to Internet use, while controlling for sociodemographic and disability-related characteristics.

Start of text box

Textbox 1: Internet Use

While the Accessibility Barriers module in the 2022 Canadian Survey on Disability (CSD) assesses barriers based on three types of online activities, the Internet Use module provides additional information regarding Internet use, which is also relevant to the “Information and Communication Technologies” priority area in the Accessible Canada Act. Specifically, the Internet Use module asks about personal Internet use (excluding business and school related use) among persons with disabilities, as well as reasons for why some may have not used the Internet in the preceding 12 months.

Of the nearly 8 million persons with disabilities aged 15 years and over, 85% reported using the Internet for personal use (from any location) within the past 12 months. The proportion was higher among persons with disabilities aged 15 to 24 years (97%), 25 to 44 years (96%) and 45 to 64 years (89%), compared with those aged 65 years and over (68%).

Of the nearly 1.2 million persons with disabilities aged 15 years and over who did not use the Internet during the last 12 months for personal use, the two most common reasons for not using the Internet were “no need, no interest or no time” (43%) followed by “too difficult to use the Internet” (34%). Around one in seven (13%) reported that the reason was “limited due to health condition”, one in ten (10%) reported “cost of service or equipment”, and less than one in ten reported “no Internet-ready device available in dwelling” (9%) or “limited or no access to Internet” (6%).

End of text box

Data and Methods

Data source

The Canadian Survey on Disability

Statistics Canada has collected data on disability for more than 30 years. Since 2012, the Canadian Survey on Disability (CSD) has been Canada’s main source of that data. The CSD provides comprehensive data on persons with disabilities for each province and territory. The survey also collects essential information on disability types and severity, supports for persons with disabilities, their employment profiles, income, education and other disability-specific information.

The survey population for the 2022 CSD was comprised of Canadians aged 15 years and over as of the date of the 2021 Census of the Population (May 2021) who were living in private dwellings. It excludes those living in institutions, on Canadian Armed Forces bases, on First Nations reserves, and those living in collective dwellings. As the institutionalized population is excluded, the data, particularly for the older age groups, should be interpreted accordingly.

The CSD uses Disability Screening Questions (DSQ) which are based on the social model of disability (Grondin, 2016). This model defines disability as the relationship between body function and structure, daily activities, and social participation, while recognizing the role of environmental factors. In keeping with this framework, the CSD targeted respondents who not only have a difficulty or impairment due to a long-term condition or health problem but also experience limitations in their daily activities. The CSD definition of disability includes anyone who reported being “sometimes”, “often” or “always” limited in their daily activities due to a long-term condition or health problem, as well as anyone who reported being “rarely” limited if they were also unable to do certain tasks or could only do them with a lot of difficulty.

Measures^Note 

Barriers to accessibility related to Internet use

The main outcome of interest is whether at least one barrier to accessibility related to Internet use was experienced at least sometimes in the last 12 months. Using a frequency scale (not applicable, never, sometimes, often or always), CSD respondents were asked to rate how often they experienced barriers^Note  using the Internet for any of the following activities because of their condition from:

1. a) Online banking or online shopping
2. b) Online booking for appointments, services or reservations
3. c) Online access to government information, services or supports

For the purposes of this report, those who indicated that they experienced barriers “sometimes”, “often” or “always” were classified as “experienced a barrier”.

Disability-related characteristics

Disability-related factors can shape the experience of persons with disabilities with barriers to accessibility. Persons with disabilities often need certain disability supports or help from paid or unpaid caregivers to overcome barriers to accessibility in their daily lives (Allen & Mor, 1997; Berardi et al., 2021; Wray, 2024). Having unmet needs in this regard is associated with decreased ability to participate in everyday activities and lower wellbeing ratings (Casey, 2015; Shooshtari et al., 2012; Zwicker et al., 2017). Severity of disability has been shown to be a predictor of Internet use as well as for performing specific types of activities online, such as checking or sending emails or publishing content online (Duplaga, 2017). The following disability-related characteristics are included in all analyses conducted.

Disability types

The CSD collects information on ten disability types: seeing, hearing, mobility, flexibility, dexterity, pain-related, learning, developmental, mental health-related and memory. To meet the definition of a disability for a particular type, respondents must have reported being “sometimes”, “often” or “always” limited in their daily activities due to a long-term condition or health problem or reported being “rarely” limited if they were also unable to do certain tasks or could only do them with a lot of difficulty.^Note  An additional variable that counts the number of co-occurring disability types is included in the descriptive analysis.

Severity of disability

A global severity score was developed for the CSD, which was calculated for each person using: the number of disability types that a person has, the level of difficulty experienced in performing certain tasks, and the frequency of activity limitations. To simplify the concept of severity, four severity classes were established: mild, moderate, severe and very severe. Note that the name assigned to each class is intended to facilitate use of a severity score and is not a label or judgement concerning the person’s level of disability. In this report, any reference to severity is based on the global severity classes.

Unmet needs for disability supports

The 2022 CSD asks several questions regarding needs for various disability supports, including personal aids and assistive devices (e.g., canes, walkers, specialized software or architectural features in the home such as widened doorways and ramps), prescription medication, as well as access to healthcare therapies and services (e.g., counselling services, physiotherapy). In this report, an unmet need for disability supports refers to instances in which persons with disabilities need but do not have at least one type of disability support – whether it be for aids and assistive devices, medication or access to healthcare therapies and services.

Unmet needs for help with everyday activities

The 2022 CSD asks questions regarding the need for help with any of the following everyday activities: preparing meals, everyday housework, heavy household chores, getting to appointments or running errands, looking after personal finances, personal care, basic medical care at home, moving around inside a residence or other types of help. The help could be provided by family, friends, neighbours or organizations and could include both paid and unpaid work. In this report, an unmet need for help with everyday activities refers to instances in which persons with disabilities need help they don’t usually receive or need more help than they usually receive with at least one type of everyday activity.

Sociodemographic characteristics

Intersectional approaches are consistent with the social model of disability and consider how disability interacts with other social characteristics to create distinct experiences (Björnsdóttir & Traustadóttir, 2010). For example, among youth and young adults with disabilities, those belonging to racialized groups tend to have worse school and work outcomes (Lindsay et al., 2022). Previous research has demonstrated the association between Internet use and age, place of residence and income (Duplaga, 2017). Including sociodemographic factors that have their own marginalizing effects within the analysis is important to identify subpopulations who may be more likely to experience barriers to accessibility. These sociodemographic characteristics include age, gender, 2SLGBTQ+ identity, racialized groups and immigrant status.^Note 

Persons with disabilities often cite cost as a reason for unmet needs for supports (Hébert et al., 2024), therefore, income level is essential to consider when examining issues related to accessibility. Place of residence, categorized into rural areas and urban population centres of different sizes, is included since geographic factors are important to consider when examining issues related to social inclusion and participation (Keefe et al., 2006; Menec et al., 2019; Repke & Ipsen, 2020; Whelan et al., 2024).

Age was categorized into four groups: 15 to 24 years, 25 to 44 years, 45 to 64 years and 65 years and over. For gender, a two-category gender variable was used to protect the confidentiality of non-binary persons, given the relatively small size of this population in Canada. More specifically, non-binary persons have been redistributed into the “men” and “women” categories. The category of “men” includes cisgender and transgender men (and/or boys), as well as some non-binary persons, while “women” includes cisgender and transgender women (and/or girls), as well as some non-binary persons (in tables these categories are denoted as “men+” and “women+”). Using questions on sex at birth, gender identity and sexual orientation, the 2SLGBTQ+ variable includes those who reported being lesbian, gay, bisexual, pansexual, or another sexual orientation that is not heterosexual (LGB+), as well as non-binary persons and transgender women and men.^Note 

Immigrant status was categorized as immigrant, non-immigrant and non-permanent resident.^Note  "Immigrant" refers to a person who is, or who has ever been, a landed immigrant or permanent resident. Immigrants who have obtained Canadian citizenship by naturalization are included in this group.^Note  "Racialized” refers to whether a person is a visible minority as defined by the Employment Equity Act as "persons, other than Aboriginal peoples, who are non-Caucasian in race or non-white in colour". The visible minority population consists mainly of the following groups: South Asian, Chinese, Black, Filipino, Arab, Latin American, Southeast Asian, West Asian, Korean and Japanese. The non-racialized category includes those who identified as White only and excludes Indigenous people.

Income was represented by quintiles which were based on after-tax economic family income adjusted by family size.^Note  Place of residence differentiates between rural areas and populations centres of different sizes. Population centres are classified into three groups, depending on the size of their Census population: small population centres, with a Census population between 1,000 and 29,999; medium population centres, with a Census population between 30,000 and 99,999; and large urban population centres, with a Census population of 100,000 or more. Rural areas are classified as outside of population centres.

Analysis

Descriptive statistics were used to estimate the prevalence of experiencing barriers to accessibility related to Internet use in the last 12 months among persons with disabilities aged 15 years and over. In all instances, proportions are calculated based on the entire population of persons with disabilities.^Note 

Logistic regression modeling was used to identify the key factors associated with higher or lower odds of experiencing barriers to accessibility among persons with disabilities, while controlling for the effects of other disability-related and sociodemographic covariates at the same time. Given that severity accounts for each of the 10 disability types, the individual disability types are excluded from the initial logistic regression model. The inclusion of both severity and all ten disability types in a single regression model introduces multicollinearity issues. Accordingly, the ten disability type variables were assessed in a separate logistic regression model that excludes the severity variable, but controls for all other covariates.

Findings from the logistic regression analyses are reported using odds ratios (OR) and their 95% confidence intervals (CI). An odds ratio represents the ratio of the odds of an event occurring (i.e., experiencing at least one barrier to accessibility) for one group vs. the odds of the same event occurring for a reference group. Accordingly, an odds ratio tells us about the difference in odds of experiencing such barriers between groups after controlling for other predictors in the model and could point to: no difference in odds (OR=1), higher odds for a given group compared with a reference group (OR>1), or lower odds for a given group compared with a reference group (OR<1). Higher odds ratios indicate that a group is more likely to experience barriers compared with the reference group.

Interpreting odds ratio results should be done with caution. The value of odds ratio estimates determines the direction of the effect (i.e., whether a certain group has higher or lower odds of experiencing barriers) but their magnitude may vary given a different set of covariates or a different sample; they are accordingly challenging to interpret and should not be compared with odds ratios from other analyses (Norton et al., 2018).

For this report, the significance level was set at p < 0.05. All estimates were weighted to represent the Canadian population with disabilities aged 15 years and over. The bootstrap technique was used to estimate variance and 95% confidence intervals to account for the complex survey design.

Results and Discussion

Prevalence of experiencing barriers to accessibility related to Internet use

Nearly one fifth of persons with disabilities experience barriers related to Internet use

Of the nearly 8 million persons with disabilities aged 15 years and over in 2022, approximately 1.4 million (17%) experienced one or more barriers to accessibility when using Internet for a number of online activities, at least sometimes, during the last 12 months. Barriers were most commonly experienced when using the Internet for online access to government information, services or supports (13%), followed by online booking for appointments, services or reservations (11%) and online banking or shopping (9%) (Chart 1).^Note 

Data table for Chart 1

Table summary
The information is grouped by Situation (appearing as row headers), Percent and 95% confidence interval, calculated using lower and upper units of measure (appearing as column headers).

Situation	Percent	95% confidence interval
		lower	upper
Notes: The overall calculation includes persons who experienced barriers using the Internet for at least one of these online activities. The categories include persons who were deemed to have experienced a barrier if they encountered it "at least sometimes" in the last 12 months. Source: Statistics Canada, Canadian Survey on Disability, 2022.
Overall	17.1	16.2	17.9
Online access to government information, services or supports	12.9	12.2	13.7
Online booking for appointments, services or reservations	11.1	10.4	11.8
Online banking or online shopping	9.3	8.6	10.0

Prevalence of experiencing barriers to accessibility related to Internet use increases with disability severity and number of co-occurring disability types

As disability severity increased, so did the proportion of persons with disabilities who experienced at least one barrier to accessibility related to Internet use. Persons with very severe disabilities (28%) were more likely to experience such barriers compared with those with mild disabilities (9%) (Table 1).

Persons with multiple disabilities were more likely to experience barriers to accessibility related to Internet use compared with those with a single disability. For example, those with four or more disability types (27%) were three times more likely to experience at least one barrier to accessibility than those with one disability type (8%).

More than seven in ten (71%) persons with disabilities have two or more co-occurring disability types (Hébert et al., 2024). The co-occurrence of disability types means that experiences of barriers may be a result of a specific disability type or a combination of disability types. As such, this report did not focus on descriptive analysis by disability type, however, prevalence of experiencing barriers to accessibility related to Internet use by disability type is presented in Chart 2 (Annex) for information purposes. Logistic regression modelling (discussed in the next section) was utilized to examine the association between each disability type and the likelihood of experiencing barriers while controlling for the effect of all other disability types (Table 3).

Persons with disabilities who had unmet needs for disability supports (i.e., aids and assistive devices, medication or healthcare therapies and services) were more likely to experience at least one barrier to accessibility related to Internet use compared with those who did not have any unmet needs (22% vs. 10%). Similarly, those who reported unmet needs for help with everyday activities were more likely to experience such barriers compared with those who had their needs met (26% vs. 12%) (Table 1).

Prevalence of experiencing barriers to accessibility related to Internet use is greater among 2SLGBTQ+ persons, immigrants and those in lower income groups

Among persons with disabilities, the proportion who experienced at least one barrier to accessibility related to Internet use varied by 2SLGBTQ+ identity, immigrant status and income level (Table 1). 2SLGBTQ+ persons with disabilities (20%) were more likely than their non-2SLGBTQ+ counterparts (17%) to experience at least one barrier to accessibility related to Internet use.

Immigrants (19%) were more likely than non-immigrants (16%) to experience at least one barrier to accessibility related to Internet use.

Encountering barriers to accessibility related to Internet use was more prevalent among persons with disabilities in lower income groups. For example, while there was no significant difference between the top two income quintiles (approximately 14%), the proportion of persons with disabilities who reported such barriers progressively increased to reach 20% among those in the lowest income quintile.

With regards to age, it is worth noting that a significant difference was observed only for those aged 45 to 64 years, where they were more likely (19%) to experience at least one barrier to accessibility related to Internet use compared with those aged 15 to 24 years (16%).

Key factors associated with the likelihood of experiencing barriers to accessibility related to Internet use

While descriptive analyses highlighted how some groups are more likely to experience barriers than others, they do not simultaneously account for other characteristics that may influence the likelihood of experiencing barriers. Logistic regression modeling was used to identify the key factors associated with the likelihood of experiencing barriers to accessibility among persons with disabilities, while controlling for the effect of other disability-related and sociodemographic covariates at the same time.

More severe disabilities and unmet needs for disability supports or help with everyday activities are associated with higher odds of experiencing barriers

The importance of disability-related factors was further confirmed by the logistic regression modelling. After controlling for other covariates, the odds of experiencing at least one barrier to accessibility related to Internet use increased with severity of disabilities. Compared with persons with mild disabilities, those with very severe disabilities faced nearly three times higher odds (OR=2.8; 95% CI: 2.3, 3.5) of experiencing such barriers (Table 2). When all other factors were considered, persons with disabilities who reported at least one unmet need for disability supports (OR=1.8; 95% CI: 1.6, 2.1) had higher odds of experiencing barriers to accessibility compared with persons who had their needs met for these supports. Likewise, persons with at least one unmet need for help with everyday activities had higher odds (OR=1.7; 95% CI: 1.5, 2.0) of experiencing at least one barrier compared with those who had their needs met. Qualitative research has demonstrated that access to different types of support (e.g., assistive technology, social support) can impact the everyday participation of persons with disabilities (Hammel et al., 2015). Having unmet needs in this regard can effectively prevent persons with disabilities from using the Internet or make it a much more challenging experience. Websites and content on the Internet are often not designed with accessibility needs in mind and may not always be compatible with available assistive devices (Botelho, 2021).

The likelihood of experiencing barriers is higher among 2SLGBTQ+ persons and is lower among women

When it comes to sociodemographic covariates, gender and identifying as 2SLGBTQ+ were significant predictors of experiencing at least one barrier to accessibility related to Internet use, after controlling for other covariates. While significant differences by immigrant status and income level were observed in the descriptive analyses, these differences did not persist when all other variables were held constant.

2SLGBTQ+ persons with disabilities had higher odds (OR=1.2; 95% CI: 1.0, 1.6) of experiencing such barriers compared with non-2SLGBTQ+ persons with disabilities. Women with disabilities had lower odds (OR=0.8; 95% CI: 0.7, 0.9) of encountering barriers related to Internet use than men with disabilities. Although women have generally been identified as vulnerable to digital exclusion (Raihan et al., 2024), some research has indicated that women with disabilities have higher rates of Internet use for services such as online banking and shopping, and are less likely to feel digitally excluded than men with disabilities (Johansson et al., 2021).

Developmental, learning and memory disability types are associated with experiencing barriers

Given that 71% of persons with disabilities have two or more co-occurring disability types (Hébert et al., 2024), the effect of each disability type must be determined while controlling for the effects of all other disability types. Using a separate logistic regression model, the likelihood of encountering at least one barrier to accessibility related to Internet use was examined when considering all ten disability types as predictors and controlling for other covariates.

The odds of experiencing barriers related to Internet use were higher among those developmental (OR=1.8; 95% CI: 1.4, 2.3), learning (OR=1.8; 95% CI: 1.5, 2.2), or memory (OR=1.8; 95% CI: 1.5, 2.2) disabilities (Table 3). Other disability types that were associated with higher odds of experiencing barriers were mental health-related (OR=1.4; 95% CI: 1.2, 1.6), seeing (OR=1.3; 95% CI: 1.1, 1.5), dexterity (OR=1.3; 95% CI: 1.0, 1.6) and hearing (OR=1.2; 95% CI: 1.0, 1.5). No significant difference in odds was found based on having pain-related disabilities. Persons with intellectual^Note  disabilities often face digital exclusion with research indicating they have among the lowest rates of Internet access and highest rates of reported difficulties using the Internet (Johansson et al., 2021). Functional, economic, attitudinal and educational barriers to Internet use not only limit their access to the Internet, but also make navigating the Internet a more difficult task. For example, persons with intellectual disabilities may have specific accessibility requirements that have often been overlooked and not reflected in design and policy decisions. In addition, a lack of awareness and training among caregivers may lead them to block access to Internet for persons with intellectual disabilities for fear that may lead to more problems than benefits (Chadwick et al., 2013).

Conclusion

This report demonstrates the importance of considering severity and type of disability, unmet needs and 2SLGBTQ+ identity when examining experiences of barriers to accessibility related to Internet use among persons with disabilities. By identifying disability-related and sociodemographic factors that place persons with disabilities at higher risk of experiencing such barriers, these findings can inform policies and programs aimed at equitable Internet accessibility and use. For example, the association between unmet needs for disability supports or help with everyday activities and the likelihood of experiencing barriers suggests that interventions aimed at addressing unmet needs could be effective in enhancing digital inclusion of persons with disabilities. In addition, narrowing the digital divide for persons with disabilities requires considering digital accessibility as a dynamic process involving training, hardware, software, content and standards (Botelho, 2021).

Further research is needed to examine the various ways in which such barriers to accessibility are experienced within different contexts and for different subpopulations, utilizing both quantitative and qualitative methods. Similarly, more qualitative research can inform our understanding of lower odds of experiencing barriers among women and higher odds among 2SLGBTQ+ persons with disabilities. Future research would also benefit from taking a broader approach to Internet use by including a wider range of common online activities.

Annex

Data table for Chart 2

Table summary
The information is grouped by Disability type (appearing as row headers), Percent and 95% confidence interval, calculated using lower and upper units of measure (appearing as column headers).

Disability type	Percent	95% confidence interval
		lower	upper
Notes: Persons were deemed to have experienced a barrier if they encountered it “at least sometimes” in the last 12 months. Significance tests were not performed since the disability types are not mutually exclusive groups. When analyzing the prevalence of encountering barriers to accessibility based on disability type, it’s important to practice caution when interpreting the data as persons with disabilities often have multiple co-occurring disability types. Source: Statistics Canada, Canadian Survey on Disability, 2022.
Seeing	22.1	20.2	24.0
Hearing	21.4	19.4	23.5
Mobility	22.0	20.0	23.6
Flexibility	22.7	21.1	24.3
Dexterity	25.5	23.2	28.0
Pain-related	19.4	18.2	20.6
Learning	29.6	27.5	31.8
Developmental	30.2	26.5	34.1
Mental health-related	22.0	20.6	23.5
Memory	32.4	29.9	35.0

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