Rationale for the Do-IT Neurodiversity University Profiler
Universities are under greater pressure than ever to get support to the right student in a timely manner. Retention and completion rates are also important measures of the performance of institutions and higher education systems. Understanding the causes of student non-completion is vital for an institution seeking to increase the chances of student success. An additional measure is graduate progression and a readiness for employment.
In the general population around 15% of individuals are described as being neurodivergent. A recent report from 2023 on student experience and neurodiversity shows more than 14 per cent of current university applicants report having Attention Deficit Hyperactivity Disorder (ADHD) and/or are autistic.
The data
The report ‘An asset not a problem: Meeting the needs of neurodivergent students’ is based on a survey of more than 2,000 university applicants across the UK, as well as a focus group with neurodivergent students currently studying at University of Bristol. This is in tandem with increasing waiting lists across the UK for adult ADHD and Autism Spectrum Condition diagnoses.
In this report just over half of neurodivergent students (52%) had experienced depression and almost two thirds (63%) experienced anxiety in the last two years, which were well above the average for all applicants. They were also more likely than average to have experienced OCD (Obsessive Compulsive disorder), eating disorders, personality disorders and PTSD (Post-Traumatic Stress Disorder).
Recent report on levels of students with disabilities in English universities. The number of students in higher education with a known disability has increased. In 2019/20 332,300 UK higher education students said that they had a disability of some kind, this was 17.3% of all home students. The number of students with a known disability has increased by 106,000 or 47% since 2014/15. Much of this increase has been in those reporting a mental health condition.
Disabled people are underrepresented in higher education and disabled students in higher education have somewhat worse outcomes from higher education than non-disabled students. Students with a disability are more likely to drop-out of courses and those that finish their degree tend to have lower degree results; in 2016/17 a lower proportion of UK disabled students were awarded a first or upper second-class degree than those without a reported disability. Employment outcomes are also worse for disabled students than for non-disabled students. These findings hold even after other factors such as prior attainment, gender, age, and ethnicity are considered.
Doing the best, we can
There is little doubt that every university wants to do their best for all their students. Understanding the needs of each learner from Day 1 can make the real difference in gaining confidence and the ability to thrive and gain the best outcome from the university experience and future employability.
The Equality Act 2010 emphasizes the responsibility of an institution “to make reasonable adjustments so disabled people can take part in education, use services, and work.” However, the law does not dictate what these adjustments are. It is therefore down to universities to be proactive in providing personalized support to its (Specific Learning Difficulty) neurodivergent students to enable them to flourish.
Rationale for Do-IT Profiler
There is extensive evidence that neurodevelopmental conditions including Dyslexia, Dyscalculia, Developmental Coordination Disorder (DCD) , Attention-Deficit/Hyperactivity Disorder (ADHD, including ADD), Autism Spectrum Disorder/Condition, Developmental Language Disorder (DLD)(including speech and language difficulties) and Tic Disorders (including Tourette’s Syndrome and Chronic Tic Disorder) co-occur and have a considerable impact on individual.
Prevalence of Neurodiversity
Neurodivergent traits are common within the general population. For example, among UK and international children estimates of the diagnosed prevalence of ADHD are-2.2-4 % whereas for Dyslexia these range around 5-10% and for Developmental Coordination Disorder from 2-5% depending on the screening tools used and definitions. (Cleaton and Kirby, 2018).
Defining Neurodivergent conditions
One of the key issues regarding Neurodiversity is that diagnosis is made on the basis of a set of symptoms – using, for example, include DSM-5 (APA, 2013) and ICD-10 (WHO, 1993) international sets of criteria. However, other factors, such as Adverse Childhood Experiences (ACEs, including childhood abuse) and head injuries (potentially causing Traumatic Brain Injury (TBI)) may also result in attention, concentration, social, memory, cognition and/or other difficulties that can mimic Neurodiverse conditions (Babikian et al., 2015; Chang et al., 2018; Van Der Kolk, 2005; Yang et al., 2016). Neurodivergent conditions, TBI and ACEs are also highly co-occurrent (Cleaton and Kirby, 2018) and symptoms of one may be misdiagnosed as another (e.g. Bishop et al., 2008).
Co-occurrence is the rule.
Co-occurrence across conditions is very common so many students will have several neurodivergent traits, and some may have a pattern that does not fall into one specific diagnostic silo.
Not everyone has a diagnosis or knows they have a neurodivergent trait or learning difference.
It can be hard for universities to predict the real prevalence of neurodivergent students coming into higher education. Often, we offer specific tick boxes for self-disclosure which may include some conditions but not all and don’t always recognise the reality that not everyone fits into one diagnostic box but may have several traits across conditions that require some adjustments.
Missing and misdiagnosed students
We can assume that students will tell you and know they have support needs. We know that many of them will have been missed, misdiagnosed, and misunderstood before arriving at university. For example, mature students returning to education, females and those coming with less social opportunity for a formal diagnosis are less likely to have gained a formal diagnosis and may not to know how to get support or what to ask for. It is these students who have a greater risk of ‘fall out’.
Potential impact of missed diagnoses and misdiagnosis.
Neurodivergent individuals will be all different but more often experience cumulative adversity – increasing, accumulating negative experiences over time. This includes a range of poor psychosocial outcomes relating to offending behaviour, physical and mental health, education and employment (Cleaton and Kirby, 2018).
One brain – mental health and ND traits often overlap.
Neurodivergent people are at increased risk of mental health difficulties (Cleaton and Kirby, 2018). These include common conditions such as Anxiety Disorders and Depression, but also a range of other conditions such as Eating Disorders, Obsessive-Compulsive Disorder, Personality Disorders and Schizophrenia (Cleaton and Kirby, 2018). This may mean there are multiple access points where they may enter services and again is dependent of the knowledge of those gatekeepers such as General Practitioners.
Neurodivergent conditions are also associated with elevated risk of suicide. Suicidal ideation, suicide attempts and suicide completion are all more frequent in individuals with ADHD (Impey and Heun, 2012) and individuals with ASD are 7.6 times as likely to die by suicide as general population controls (Hirvikoski et al., 2016).
Individuals with Dyslexia and/or Dyscalculia are 2.2 times as likely as controls to have ever attempted suicide (Fuller-Thomson et al., 2018). As well as suicide attempts, Dyslexia is also associated with self-harm and suicidal ideation (Alexander-Passe, 2016).
Varying levels of provision of services
Services are under pressure and in siloes.There are also ‘postcode lotteries’ affecting the provision of diagnostic services in the UK (Lamb, 2018) and increasing waiting lists in many areas of the UK.
The diagnosis an individual receives remains, in many cases, determined by the services provided by their local healthcare board (Ross, 2018), the knowledge and biases of parents and of gatekeepers such as teachers and GPs (Miyasaka et al., 2018), for those in education.
In addition to this is the knowledge of the specialists that are seen (Astle and Bathelt, 2019) and the ability of the individual and/or their parents to access services (Keenan et al., 2010). Some less well-known conditions, such as DCD and DLD, often fail to be considered and assessments for these may be particularly difficult to access (Missiuna et al., 2006).
Missing out if you fall below the line
As well as commonly co-occurring, Neurodivergent conditions also commonly exist at a sub-threshold level. A dimensional approach is similarly to other human characteristics such as height, any cut-off between ‘normal’ or ‘typical’ and ‘abnormal’, ‘disordered’ or ‘atypical’ is arbitrary. It is quite common, for example, for individuals with ADHD or DLD to have what is described as ‘Autistic tendencies’ – i.e. sub-threshold ASD (Conti-Ramsden et al., 2006; Green et al., 2015). Some individuals, despite having
functional difficulties in many areas (e.g. attention, social communication, reading, mathematics, memory), do not reach the diagnostic threshold for any of the particular conditions associated with these difficulties. Some people are referred to as the ‘missing middle’ who keep missing out again and again on support. This is because service design and support is usually based on dimensional, not categorical characteristics with minimum thresholds to be met to have access to support.
Diagnostic thresholds can result in inequitable provision of support and services, as they mean diagnosis functions as an all-or-nothing model. They also rarely consider other contributing factors such as TBI, abuse experiences and family disadvantage, when diagnosing.
The cumulative challenges experienced by someone with symptoms of multiple Neurodivergent traits at a sub-threshold level may be functionally more impairing than the challenges experienced by someone who meets diagnostic criteria for, and has symptoms of, a single condition only.
However, without a diagnosis, individuals with sub-threshold Neurodiverse conditions are rarely deemed eligible for educational or medical support even if their overall needs are potentially greater. In a prison or youth offending context, this may be why so few people have received diagnosis – their pattern of needs don’t fit the current, categorical service model.
Taking a person-centred, needs-led approach.
Categorical approaches to the diagnosis and support of Neurodivergent individuals have many serious shortcomings. An alternative to these, are dimensional approaches – approaches which consider an individual’s unique needs, rather than determining whether the individual fits certain diagnostic criteria and providing support only if these diagnostic criteria are met.
Dimensional approaches are needs-led rather than diagnosis-led.
A shift away from categorical approaches towards more holistic, profile-based, dimensional approaches have been suggested by some psychiatrists (NIMH, 2014). This approach creates a formulation-based assessment and management plan based on an individual’s needs, whether they group neatly into diagnostic criteria or not. The key areas of challenge for that individual are identified with respect to their current social and physical environment and appropriate, holistic support is provided.
Dimensional approaches are also typically person-centred – they put the person first and consider them as an individual rather than a category or type.
Person-centred approaches are often based on a biopsychosocial model of disability. This model incorporates the best aspects of two previous models of disability: the medical model and the social model (Engel, 1977, 1980). It is based on the idea that disability is the combination of differences in people’s bodies (bio-), differences in people’s minds (psycho-) and the mismatch between people’s needs and the physical and social environment that they live in (social).
By taking person-centred approach using the biopsychosocial model, we can better support Neurodivergent people, resulting in better outcomes for all.
What is Do-IT Profiler in a University setting?
Profiler is a tried and tested modular system on a management platform built for the context of a university setting. It contains a series of modules that can be completed at different levels e.g., all students, some students, for specific support needs.
While profiler focuses on neurodiversity it also includes other modules for study skills and aid with organisation and time management, wellbeing, and preparation for the workplace.
It is presented in short modules that are not overwhelming and not all the modules need being completed by all students.
This is an accessible, scalable, and translatable tool that can reach large numbers to provide person centred and practical strategies during their time at university.
It is equitable not depending on who shouts loudest. This aids participation, retention, wellbeing and takes a student centred approach.,
How can Profiler help your university?
Students
- Accessible and practical person-centred guidance from Day 1.
- Identifying the student’s strengths across cognitive domains as well as minimising any ND challenges they may be experiencing.
- Aiding study skills with different tools for both under and postgraduate students
- Guidance for helping with mental well-being.
- Can be used to help the student preparing for work placements, apprenticeships and for the workplace itself.
- Can guide the student to consider if further assessment is necessary or required.
- Translatable for students where English is not their first language.
Lecturers
- Quick overall picture of a group of students to know areas of strengths and challenges from the start of the academic year.
- Easy to view report of students if a more in-depth conversation is required.
Student Support Services
- Saves time collating information leading to more one- to- one support focusing on the challenges.
- Data available for future planning or monitoring.
- Easy to access data to identify more vulnerable students.
- Reduces administrative time and burden.
- Can provide links to resources and services within the university and in the locality.
- Can easily design effective group support because of the live and collated data.
The University
- Takes an inclusive approach.
- Fits with a widening participation agenda and United Nation SDG targets.
- Can reduce the burden on student services and help staff support all students effectively and economically.
- Measures change over time at a macro level.
- Triages support in an equitable manner.
- ROI- effectively help to retain students and create better outcomes.
- Data to inform planning and measure outcomes.
- Reassured that secure data is on Microsoft Azure and Do-IT has Cyber Essentials Plus certification.
- Easy to use dashboard to have data at all levels.
References:
Meeting the Needs of Neurodivergent Students:
Support for disabled students in higher education in England:
https://dera.ioe.ac.uk/id/eprint/37466/1/CBP-8716%20(2)%20(redacted).pdf
Overlap with conditions:
Waiting lists: https://www.bmj.com/content/380/bmj.p324/rr
English Disability data for Universities: https://explore-education-statistics.service.gov.uk/find-statistics/special-educational-needs-in-england
Are you supporting apprenticeships too?
Learning Support Funding (LSF) can help if you are delivering apprenticeship programmes
Providers are now required to “undertake a screening exercise for learning support.” (DfE, 2023)
Initial assessment of potential learning difficulties and the impact these may have on the learner’s chances of success is more and more becoming part of the expected process for providers.
We can assume that apprentices will tell you and know they have support needs. We know that many apprentices will have been missed, misdiagnosed and misunderstood. These apprentices may be at greater risk of ‘fall out’ too.
The Department for Education (DfE) policy guidelines highlight the fact that not everyone has a diagnosis or will know….
“In relation to paragraphs 26 to 27, this can include an apprentice who has not previously had a learning difficulty or disability identified, but in relation to whom the provider has identified a learning difficulty or disability (as defined in Section 15ZA(6) of the Education Act 1996) that would directly affect the apprentice’s ability to complete their apprenticeship.” (DfE, 2023)
If you are an apprenticeship provider, did you know you can claim Learning Support Funding (LSF) to help you cover the additional costs of supporting learners who have learning difficulties, neurodivergent conditions or disabilities. Importantly this includes those who have not been previously diagnosed.
Latest guidance on funding rules have been published for 2023-2024. The specific rules are found here.
There is also has been a greater focus on SEND (Special Educational Needs and Disabilities),
LSF and Earnings Adjustment Statement (EAS)
LSF gives you extra funding each month to support a learner with identified needs. You can also use the Earnings Adjustment Statement (EAS) to claim funding that you cannot report in the Individual Learning Report (ILR) for larger, one-off support costs.
Not all providers have confidence in claiming the funding. This can mean they can miss out on the resources to help cover the costs of ant additional support that can be in place for learners. Support can make a huge difference to outcomes for the learner not only during the time as an apprentice but can have a long term impact.
Organisations can claim £150 per month per learner (or more) if you can demonstrate that the learner needs more intensive support. The funding is used for adjustments and this include training, and providing assistive technology.
Getting LSF can help your organisation to deliver personalised support from day 1 to increase retention rates and create better outcomes
How can you do this?
Identify the learners who need learning support and assess their needs. The University Do-IT Profiler has been designed for you. It allows your organisation to assess each learner’s individual’s needs at scale. The platform delivers personalised reports and can map your learner’s journey. This means you can deliver support and adjustments from day 1. It provides information to the employer and trainer to upskill them too.
Plan and deliver the learning support that meets the needs of each learner.
Record and evidence the learning support that you provide. You need to keep a record of the learner’s needs, the support plan, the delivery and the outcomes. Review and evidencing the plan has been enacted is very important.
Claim the learning support funding through the ILR. You also need to keep all the records and evidence for audit purposes.All providers on the register of apprenticeship training providers (RoATP) should be constantly evaluating their support infrastructure to ensure they provide outstanding ways of meeting their learners’ needs.
How can you support your apprentices ?
- Identify the learners who need learning support and assess their needs. The Do-IT Neurodiversity Apprentice platform has been designed for you. It allows your organisation to assess each learner’s individual’s needs at scale. The platform delivers personalised reports and can map your learner’s journey. This means you can deliver support and adjustments from day 1. It provides information to the employer and trainer to upskill them too.
- Plan and deliver the learning support that meets the needs of each learner.
- Record and evidence the learning support that you provide. You need to keep a record of the learner’s needs, the support plan, the delivery and the outcomes. Review and evidencing the plan has been enacted is very important.
- Claim the learning support funding for apprentices through the ILR. You also need to keep all the records and evidence for audit purposes. All providers on the register of apprenticeship training providers (RoATP) should be constantly evaluating their support infrastructure to ensure they provide outstanding ways of meeting their learners’ needs.
What can you also do ….
- Training staff/tutors to support learners with neurodivergent traits – Do-IT provides a range of training options for your staff from awareness sessions to a suite of e-learning materials.
- Regular meetings and check ins with their support staff.
- Assistive tech support with appropriate training
More References
Alexander-Passe, N. (2016), “Dyslexia: Investigating Self-Harm and Suicidal Thoughts/Attempts as a Coping Strategy”, Journal of Psychology & Psychotherapy, Vol. 5 No. 6, p. 1000224.
APA. (2013), Diagnostic and Statistical Manual of Mental Disorders, 5th ed., American Psychiatric Publishing, Washington, DC.
Astle, D.E. and Bathelt, J. (2019), “Remapping the cognitive and neural profiles of children who struggle at school”, Developmental Science, Vol. 22, p. e12747.
Biederman, J., Mick, E., Faraone, S. V, Braaten, E., Doyle, A., Spencer, T., Wilens, T.E., et al. (2002), “Influence of Gender on Attention Deficit Hyperactivity Disorder in Children Referred to a Psychiatric Clinic”, American Journal of Psychiatry, Vol. 159 No. 1, pp. 36–42.
Bihlar Muld, B., Jokinen, J., Bölte, S. and Hirvikoski, T. (2015), “Long-term outcomes of pharmacologically treated versus non-treated adults with ADHD and substance use disorder: A naturalistic study”, Journal of Substance Abuse Treatment, The Authors., Vol. 51, pp. 82–90.
Bishop, D.V.M., Whitehouse, A.J.O., Watt, H.J. and Line, E.A. (2008), “Autism and diagnostic substitution: evidence from a study of adults with a history of developmental language disorder”, Developmental Medicine & Child Neurology, Vol. 50 No. 5, pp. 341–345.
Braiden, H.J., Bothwell, J. and Duffy, J. (2010), “Parents’ experience of the diagnostic process for Autistic Spectrum Disorders”, Child Care in Practice, Vol. 16 No. 4, pp. 377–389.
Butwicka, A., Långström, N., Larsson, H., Lundström, S., Serlachius, E., Almqvist, C., Frisén, L., et al. (2017), “Increased Risk for Substance Use-Related Problems in Autism Spectrum Disorders: A Population-Based Cohort Study”, Journal of Autism and Developmental Disorders, Springer US, Vol. 47 No. 1, pp. 80–89.
Capusan, A.J., Bendtsen, P., Marteinsdottir, I. and Larsson, H. (2019), “Comorbidity of adult ADHD and its subtypes with Substance Use Disorder in a large population-based epidemiological study”, Journal of Attention Disorders, Vol. 23 No. 12, pp. 1416–1426.
Chang, H.K., Hsu, J.W., Wu, J.C., Huang, K.L., Chang, H.C., Bai, Y.M., Chen, T.J., et al. (2018), “Traumatic Brain Injury in early childhood and risk of Attention-Deficit/Hyperactivity Disorder and Autism Spectrum Disorder: a nationwide longitudinal study”, Journal of Clinical Psychiatry, Vol. 79 No. 6, available at:https://doi.org/10.4088/JCP.17m11857.
Cleaton, M.A.M. and Kirby, A. (2018), “Why Do We Find it so Hard to Calculate the Burden of Neurodevelopmental Disorders?”, Journal of Childhood & Developmental Disorders, Vol. 4 No. 3, pp. 1–20.
Conti-Ramsden, G., Simkin, Z. and Botting, N. (2006), “The prevalence of autistic spectrum disorders in adolescents with a history of specific language impairment (SLI)”, Journal of Child Psychology and Psychiatry and Allied Disciplines, Vol. 47 No. 6, pp. 621–628.
Crane, L., Chester, J.W., Goddard, L., Henry, L.A. and Hill, E. (2016), “Experiences of autism diagnosis: A survey of over 1000 parents in the United Kingdom”, Autism, Vol. 20 No. 2, pp. 153–162.
Dean, M., Harwood, R. and Kasari, C. (2017), “The art of camouflage: Gender differences in the social behaviors of girls and boys with autism spectrum disorder”, Autism, Vol. 21 No. 6, pp. 678–689..
Engel, G.L. (1977), “The need for a new medical model: A challenge for biomedicine”, Science, Vol. 196 No. 4286, pp. 129–136.
Engel, G.L. (1980), “The clinical application of the biopsychosocial model”, American Journal of Psychiatry, Vol. 137 No. 5, pp. 535–544.
Fuller-Thomson, E., Carroll, S.Z. and Yang, W. (2018), “Suicide Attempts Among Individuals With Specific Learning Disorders: An Underrecognized Issue”, Journal of Learning Disabilities, Vol. 51 No. 3, pp. 283–292.
Gershon, J. (2002), “A meta-analytic review of gender differences in ADHD”, Journal of Attention Disorders, Vol. 5 No. 3, pp. 143–154.
Green, J.L., Rinehart, N., Anderson, V., Nicholson, J.M., Jongeling, B. and Sciberras, E. (2015), “Autism spectrum disorder symptoms in children with ADHD: A community-based study”, Research in Developmental Disabilities, Elsevier Ltd., Vol. 47, pp. 175–184.
Gudjonsson, G.H., Sigurdsson, J.F., Sigfusdottir, I.D. and Young, S. (2012b), “An epidemiological study of ADHD symptoms among young persons and the relationship with cigarette smoking, alcohol consumption and illicit drug use”, Journal of Child Psychology and Psychiatry and Allied Disciplines, Vol. 53 No. 3, pp. 304–312..
Hammerness, P., Petty, C., Faraone, S. V. and Biederman, J. (2017), “Do Stimulants Reduce the Risk for Alcohol and Substance Use in Youth With ADHD? A Secondary Analysis of a Prospective, 24-Month Open-Label Study of Osmotic-Release Methylphenidate”, Journal of Attention Disorders, Vol. 21 No. 1, pp. 71–77.
Harrop, C., Gulsrud, A. and Kasari, C. (2015), “Does gender moderate core deficits in ASD? An investigation into restricted and repetitive behaviours in girls and boys with ASD”, Journal of Autism & Developmental Disorders, Vol. 45 No. 11, pp. 3644–3655.
Hiller, R.M., Young, R.L. and Weber, N. (2014), “Sex differences in Autism Spectrum Disorder based on DSM-5 criteria: evidence from clinician and teacher reporting”, Journal of Abnormal Child Psychology, Vol. 42 No. 8, pp. 1381–1393.
Hirvikoski, T., Mittendorfer-Rutz, E., Boman, M., Larsson, H., Lichtenstein, P. and Bölte, S. (2016), “Premature mortality in autism spectrum disorder”, British Journal of Psychiatry, Vol. 208 No. 3, pp. 232–238.
Hofvander, B., Delorme, R., Chaste, P., Nydén, A., Wentz, E., Ståhlberg, O., Herbrecht, E., et al. (2009), “Psychiatric and psychosocial problems in adults with normal-intelligence autism spectrum disorders”, BMC Psychiatry, Vol. 9, pp. 1–9.
Impey, M. and Heun, R. (2012), “Completed suicide, ideation and attempt in attention deficit hyperactivity disorder”, Acta Psychiatrica Scandinavica, Vol. 125 No. 2, pp. 93–102.
Jawaid, A., Riby, D.M., Owens, J., White, S.W., Tarar, T. and Schulz, P.E. (2012), “‘Too withdrawn’ or ‘too friendly’: considering social vulnerability in two neuro-developmental disorders”, Journal of Intellectual Disability Research, Vol. 56 No. 4, pp. 335–350.
Jones, L., Goddard, L., Hill, E.L., Henry, L.A. and Crane, L. (2014), “Experiences of Receiving a Diagnosis of Autism Spectrum Disorder: A Survey of Adults in the United Kingdom”, Journal of Autism and Developmental Disorders, Vol. 44 No. 12, pp. 3033–3044.
Van Der Kolk, B.A. (2005), “Developmental Trauma Disorder: A new rational diagnosis for children with complex trauma histories”, Psychiatric Annals, Vol. 35 No. 5, pp. 401–408.
Lai, M.-C., Lombardo, M. V, Ruigrok, A.N. V, Chakrabarti, B., Auyeung, B., Szatmari, P., Happé, F., et al. (2017), “Quantifying and exploring camouflaging in men and women with autism”, Autism, Vol. 21 No. 6, pp. 690–702.
Lamb, N. (2018), The Autism Diagnosis Crisis: Research from Rt Hon Norman Lamb MP and the All Part Parliamentary Group on Autism Uncovers Stark Regional Variation and Long Waits for Autism Diagnosis, London, UK.
Loucks, N. (2007a), No One Knows: Offenders with Learning Difficulties and Learning Disabilities – Review of Prevalence and Associated Needs, London, UK, available at: http://www.ohrn.nhs.uk/resource/policy/NoOneKnowPrevalence.pdf.
Loucks, N. (2007b), No One Knows: Offenders with Learning Difficulties and Learning Disabilities: The Prevalence and Associated Needs of Offenders with Learning Difficulties and Learning Disabilities, London, UK.
Mandy, W., Chilvers, R., Chowdhury, U., Salter, G., Seigal, A. and Skuse, D. (2012), “Sex differences in Autism Spectrum Disorder: evidence from a large sample of children and adolescents”, Journal of Autism & Developmental Disorders, Vol. 42 No. 7, pp. 1304–1313.
McManus, S., Bebbington, P., Jenkins, R. and Brugha, T. (2016), Mental Health and Wellbeing in England: Adult Psychiatric Morbidity Survey 2014, Leeds, UK, available at: https://files.digital.nhs.uk/pdf/q/3/mental_health_and_wellbeing_in_england_full_report.pdf.
McManus, S., Meltzer, H., Brugha, T., Bebbington, P. and Jenkins, R. (2009), Adult Psychiatric Morbidity in England, 2007: Results of a Household Survey, Leeds, UK.
Missiuna, C., Moll, S., Law, M., King, S. and King, G. (2006), “Mysteries and mazes: parents’ experiences of children with developmental coordination disorder.”, Canadian Journal of Occupational Therapy, Vol. 73 No. 1, pp. 7–17.
Parkin, E., Kennedy, S., Bate, A., Long, R., Hubble, S. and Powell, A. (2018), Learning Disability : Overview of Policy and Services, London, UK, available at: https://researchbriefings.files.parliament.uk/documents/SN07058/SN07058.pdf.
Ross, C. (2018), “NHS Grampian accused of ‘shocking’ discrimination against adults with ADHD”, The Press and Journal, available at: https://www.pressandjournal.co.uk/fp/news/aberdeen/1512913/nhs-grampian-accused-of-shocking-discrimination-against-adults-with-adhd/ (accessed 28 January 2019).
Rucklidge, J.J. (2010), “Gender differences in Attention-Deficit/Hyperactivity Disorder”, The Psychiatric Clinics of North America, Vol. 33 No. 2, pp. 357–373.
Sedgewick, F., Hill, V., Yates, R. and Pickering, L. (2016), “Gender Differences in the Social Motivation and Friendship Experiences of Autistic and Non-autistic Adolescents”, Journal of Autism and Developmental Disorders, Springer US, Vol. 46 No. 4, pp. 1297–1306.
Sofronoff, K., Dark, E. and Stone, V. (2011), “Social vulnerability and bullying in children with Asperger syndrome”, Autism, Vol. 15 No. 3, pp. 355–372.
Sprafkin, J., Gadow, K.D., Weiss, M.D., Schneider, J. and Nolan, E.E. (2007), “Psychiatric comorbidity in ADHD symptom subtypes in clinic and community adults”, Journal of Attention Disorders, Vol. 11 No. 2, pp. 114–124.
Wechsler, D. (1999), Wechsler Adult Intelligence Scale – 3rd UK Edition (WAIS-III UK), Oxford, UK.
WHO. (1993), The ICD-10 Classification of Mental and Behavioural Disorders: Clinical Descriptions and Diagnostic Guidelines, World Health Organisation, Geneva.
Van Wijngaarden-Cremers, P.J.M., Van Eeten, E., Groen, W.B., Van Deurzen, P.A., Oosterling, I.J. and Van Der Gaag, R.J. (2014), “Gender and Age Differences in the Core Triad of Impairments in Autism Spectrum Disorders: A Systematic Review and Meta-analysis”, Journal of Autism & Developmental Disorders, Vol. 44 No. 3, pp. 627–635.
Wilens, T.E., Martelon, M., Joshi, G., Bateman, C., Fried, R., Petty, C. and Biederman, J. (2011), “Does ADHD predict substance use disorders? A 10-year follow-up study of young adults with ADHD”, Journal of the American Academy of Child & Adolescent Psychiatry, Vol. 50 No. 6, pp. 543–553.
Yang, L.-Y., Huang, C.-C., Chiu, W.-T., Huang, L.-T., Lo, W.-C. and Wang, J.-Y. (2016), “Association of traumatic brain injury in childhood and attention-deficit/hyperactivity disorder: a population-based study”, Pediatric Research, Vol. 80 No. 3, pp. 356–362.
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