Autistic Traits in Doctors: A Cross-sectional Study of Doctors Seeking Mental Health Support

Autistic Traits in Doctors: A Cross-sectional Study of Doctors Seeking Mental Health Support | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Autistic Traits in Doctors: A Cross-sectional Study of Doctors Seeking Mental Health Support Liren Abeyratne, Ken Courtenay, Zaid Al-Najjar, Sebastian C K Shaw, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7437542/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Neurodevelopmental conditions are increasingly recognised as important to understanding mental health difficulties. However, the presence of autism among doctors experiencing mental health problems remains underexplored. This study aims to estimate the prevalence of autistic traits among doctors seeking mental health support and examine associations with demographic characteristics, medical speciality, and co-occurring mental health and neurodevelopmental conditions. Methods We conducted a cross-sectional analysis of self-reported data from doctors who accessed NHS Practitioner Health between April and July 2024. The Autism Spectrum Quotient (AQ-10) screened for autistic traits, alongside validated measures of depression (PHQ-9), anxiety (GAD-7), psychological distress (CORE-10), and ADHD traits (ASRS-6). Logistic regression was used to assess associations with demographic, clinical, and speciality variables. Results Of the 946 doctors included, 13.6% screened positive for autism using the AQ-10. Positive screening was significantly associated with male gender (OR = 1.68, p = 0.008), age 22–29 (OR = 1.84, p = 0.016), working in psychiatry (OR = 2.48, p = 0.010), and surgical specialities (OR = 2.07, p = 0.037). AQ-10 positivity was also associated with significantly higher odds of anxiety, depression, psychological distress, and screening positive for ADHD. Discussion This study found a notably high prevalence of autistic traits among UK doctors seeking mental health support which raises the possibilities of under-recognition and possible masking within this workforce. Associations with gender, specialty, and co-occurring psychiatric symptoms highlight the need for earlier identification and tailored support systems. Findings are limited by reliance on self-report screening and a restricted sample of doctors actively seeking psychological support, limiting generalisability. Nevertheless, this study provides novel insights into neurodiversity in medicine. Conclusions A substantial proportion of doctors seeking mental health support screened positive for autistic traits with notable psychiatric and other neurodevelopmental comorbidity. These findings highlight the importance of considering underlying autism when assessing and supporting doctors with mental health difficulties. Autism Doctors Mental health Neurodiversity Background Mental health difficulties are an increasingly pressing concern for the healthcare workforce, with significant implications for clinician wellbeing, workforce retention, and patient care (1,2). Data from NHS England (March 2025) indicate that 27.5% of all health staff absences are due to anxiety, stress, depression, or other psychiatric conditions (3). Doctors, despite their role as health care professionals, are not immune to these mental health difficulties. It has been suggested that the high-pressure nature of clinical environments places doctors at elevated risk for psychiatric disorders, including depression, anxiety, and occupational burnout (4). In the UK, the prevalence of mental health difficulties among doctors has been reported to range from 17% to 52% (5), with contributing factors including long working hours, emotionally demanding roles, and the residual impact of the COVID-19 pandemic (6,7). These mental health conditions not only diminish personal well-being but also impact workplace performance, contributing to burnout, medical errors, absenteeism from work, and a reduced quality of medical care (1,2). There is a growing understanding of mental disorders such as anxiety, depression, and burnout among doctors (8,9); however, the understanding of neurodevelopmental conditions such as autism and Attention-Deficit/Hyperactivity Disorder (ADHD) in this population is limited. A recent analysis of ADHD among doctors accessing mental health support revealed that approximately 35% of those doctors screened positive using the Adult ADHD Self-Report Scale (ASRS), a rate substantially higher than the estimated 2–3% prevalence of ADHD in the general adult population (10,11). A recent meta-analysis reported a 40.2% lifetime prevalence of ADHD among autistic people, indicating a significant overlap between these neurotypes (12). This raises the possibility that we may be missing unrecognised autism in some doctors experiencing mental health difficulties. A survey by the Royal College of General Practitioners (RCGP) Clinical Priority Group reported that 1% of general practitioners self-identified as autistic (13), whereas another survey of psychiatrists (14) revealed that 2 (1.2%) of 172 respondents explicitly reported being autistic themselves. These values closely mirror the 1.1% prevalence in the general adult population of England (15). However, the growing number of doctors joining autism-specific support networks and peer communities suggests that many more may be undiagnosed or only recently recognise their neurodivergence (16,17). There is currently a lack of data on whether the presence of autism contributes to the mental health challenges that doctors face. Considering existing evidence, it can be hypothesised that neurodivergent doctors with conditions such as autism and ADHD may face unique challenges in the workplace (18,19). Differences in social communication, sensory sensitivity, and cognitive processing often require greater mental effort to function in neurotypical environments (20). Masking behaviours can lead to exhaustion, meltdowns, or worsening of co-occurring mental health conditions when coping mechanisms are overwhelmed (21). Communication differences are often dismissed as quirks or overlooked due to compensatory strengths, which can delay recognition and support (22). Therefore, it is crucial to understand the role that being autistic may play among doctors experiencing mental health problems not only to improve the support and retention of neurodivergent doctors but also to foster a more inclusive and healthier medical workforce. This study aims to Assess the prevalence of autistic traits among doctors with mental health difficulties, using a validated screening tool for ASD (AQ-10). Evaluate the association between screening positive for autism and demographic factors as well as medical specialities. Explore co-occurring neurodevelopmental or mental health conditions among doctors screening positive for autism. Characterise specific autistic traits most commonly described by doctors seeking mental health support. Methods We conducted a cross-sectional analysis of self-reported data from doctors who accessed NHS Practitioner Health between April and July 2024. The Autism Spectrum Quotient (AQ-10) screened for autistic traits, alongside validated measures of depression (PHQ-9), anxiety (GAD-7), psychological distress (CORE-10), and ADHD traits (ASRS-6). Logistic regression was used to assess associations with demographic, clinical, and speciality variables. Results Methodology This was a cross-sectional, observational study. Data were collected from electronic self-registration forms completed by doctors seeking support for mental health difficulties through the NHS Practitioner Health between April 2024 and July 2024. These data included demographic variables (age, gender identity, ethnicity, and medical specialty) and responses to validated self-report screening instruments for common mental disorders and neurodivergence. Mental health was assessed using the following instruments: the 9-item Patient Health Questionnaire (PHQ-9) for depressive symptoms (23), the 7-item Generalized Anxiety Disorder Scale (GAD-7) for anxiety symptoms (Johnson et al., 2019; Löwe et al., 2008), and the 10-item Clinical Outcomes in Routine Evaluation (CORE-10) for general psychological distress (24). Thresholds for a positive screen were defined as scores ≥15 on the PHQ-9 and GAD-7 and scores ≥25 on the CORE-10. ADHD symptoms were screened using the 6-item Adult ADHD Self-Report Scale v1.1 (ASRS) (25–27), with a score of 4 or above being considered positive for ADHD. Autistic traits were assessed and screened for using the 10-item Autism Spectrum Quotient (AQ-10), a brief and widely used screening tool recommended by the National Institute for Health and Care Excellence (NICE) for identifying adults who may benefit from a full diagnostic assessment (28). A score of 6 or more was considered as screening positive for autism. Five domains of AQ-10 (Social Skills, Communication, Attention to Detail, Attention Switching, and Imagination) were analysed against scoring positive for the AQ-10. Participants were included if they completed demographic information and the AQ-10 at registration. Cases with missing AQ-10 data or inaccurate dates of birth were excluded. Data analysis Descriptive statistics were used to summarise the demographic characteristics and prevalence of positive AQ-10 results. The proportions of doctors meeting the thresholds for positive screens on the PHQ-9, GAD-7, CORE-10, and ASRS were calculated for the AQ-10-positive and negative groups. Logistic regression models were used to estimate odds ratios for screening positive on the AQ-10 in relation to age, gender, ethnicity, and medical specialty as well as screening positive on the PHQ-9, GAD-7, CORE-10 and ASRS. Additional sub analyses have examined the distribution of responses across the AQ-10 items to identify patterns in specific autistic trait domains. Item-level data were also further stratified by medical specialty to explore potential domain-level differences across clinical subgroups. All analyses were conducted via R version 4.5.1 (2025-06-13 ucrt), with a significance threshold of p < 0.05. Conclusions There were 1,037 referrals from doctors who sought support for mental health concerns between April and July 2024 and who provided consent for their data to be used for research. Following the exclusion of entries with invalid date-of-birth records and missing responses to the AQ-10, a total of 946 valid entries were retained for analysis (Table 1). Table 1. Characteristics and AQ-10 Results of Doctors Accessing NHS Practitioner Health (PH) Doctors accessing NHS PH (%) AQ-10 Positive (%) AQ-10 Negative (%) All doctors 946 (100) 129 (14) 817 (86) Gender: Female 649 (69) 75 (12) 574 (88) Male 294 (31) 53 (18) 241 (82) Not Listed 3 (0) 1 (33) 2 (67) Age: 20-29 154 (16) 30 (19) 124(81) 30-39 430 (46) 50 (12) 380 (88) 40-49 245 (26) 28 (11) 217 (89) 50-59 104 (11) 18 (17) 86 (83) 60-69 13 (1) 3 (23) 10 (77) Ethnicity: Asian 240 (25) 36 (15) 204 (85) Black 49 (5) 7 (14) 42 (86) White 546 (58) 66 (12) 480 (88) Mixed 54 (6) 12 (22) 42(78) Not Listed 47 (5) 7 (15) 40 (85) Missing 10 (1) 1 (10) 9 (90) Specialty: General Practice 423 (45) 49 (12) 374 (88) General Medicine 106 (11) 8 (8) 98(92) Other 24 (3) 5 (21) 19 (79) Psychiatry 53 (5) 13 (25) 40 (75) Surgery 61 (6) 13 (21) 48 (79) Paediatrics 50 (5) 8 (16) 42 (84) Emergency Medicine 34 (4) 3 (9) 31 (91) Obstetrics and Gynaecology 24 (3) 2 (8) 22 (92) Radiology 9 (1) 3 (33) 6 (67) Intensive Care and Anaesthesia 66 (7) 8 (12) 58 (88) Pathology 9 (1) 1 (11) 8 (89) Ophthalmology 11 (1) 1 (9) 10 (91) Foundation/non specialty 71 (8) 14 (20) 57 (80) *Public Health (n=3) and Occupational medicine (n=2) specialties were removed due to small sample size, which limited statistical validity. The sample was predominantly female (69%). Doctors aged 30–39 years accounted for the largest proportion of referrals (46%), with those aged 20–39 contributing 62%. In contrast, referrals from doctors aged 50 years and above were notably lower, accounting for only 12% of the total sample. In terms of ethnicity, the majority identified as White (58%), followed by Asian (25%). Other ethnic categories were represented in smaller proportions and were grouped accordingly for statistical analysis. In terms of medical specialities, the largest group accessing mental health support were from doctors in general practice (45%), followed by general medicine (11%). A total of 13.6% (n = 129) of the sample met the threshold for a positive screen for autism on the AQ-10. Positive screens were more prevalent among male doctors (18%) than among female doctors (12%). The highest proportion of positive screens was observed among doctors in the 60–69 years age group (23%); however, this group comprised a relatively small subsample (n=13). Among the age groups with larger (>100) sample sizes, the highest rates of prevalence were in the 20–29 years age group (17%), followed by those aged 50–59 years (12%). Logistic regression analysis revealed that males were significantly more likely to screen positive on the AQ-10 than females (OR=1.68, CI=(1.14, 2.46), p=0.008) (Table 2). Doctors aged 22-29 years were also more likely to screen positive on the AQ-10 than those in the reference group (aged 30-39 years). When analysed by medical specialty, psychiatry (OR=2.48, CI=(1.20, 4.86), p=0.010) and surgical (OR=2.07, CI=(1.01, 4.00), p=0.037) specialties were significantly more likely to screen positive on the AQ-10 compared to the reference group of general practitioners. Table 2. Results from logistic regression models for the associations between the AQ10 score and demographic variables, medical specialty, and mental health indicators Characteristics Model 1 (AQ10) OR 95% CI P value Intercept 0.13 (0.10, 0.17) <2e -16 Gender: Females (ref.) Males 1.68 (1.14, 2.46) 0.008 Age: 30-39 (ref.) 22-29 1.84 (1.11, 3.00) 0.016 40-49 0.98 (0.59, 1.59) 0.938 50-59 1.59 (0.86, 2.82) 0.121 60-69 2.28 (0.50, 7.75) 0.222 P value <0.0001 Ethnicity: White (ref.) Asian 1.28 (0.82, 1.98) 0.264 Black 1.21 (0.48, 2.65) 0.654 Mixed 2.08 (1.00, 4.04) 0.038 Other 1.27 (0.50, 2.79) 0.575 Missing P Value 0.81 (0.04, 4.40) 0.841 <0.0001 Specialty: General Practice (ref.) Emergency Medicine 0.74 (0.17, 2.17) 0.627 Foundation/non specialty 1.87 (0.94, 3.54) 0.061 Intensive Care and Anaesthetics 1.05 (0.44, 2.22) 0.899 General Medicine 0.62 (0.27, 1.29) 0.234 Obstetrics and Gynaecology 0.69 (0.11, 2.45) 0.628 Ophthalmology 0.76 (0.04, 4.11) 0.799 Paediatrics 1.45 (0.60, 3.13) 0.367 Pathology 0.95 (0.05, 5.36) 0.965 Psychiatry 2.48 (1.20, 4.86) 0.010 Radiology 3.82 (0.79, 14.96) 0.278 Surgery 2.07 (1.01, 4.00) 0.037 Other 2.01 (0.64, 5.26) 0.184 P value <0.0001 ASRS: Negative (ref.) Positive 2.53 (1.74, 3.70) <0.0001 CORE-10: Negative (ref.) Positive 1.99 (1.28, 3.03) 0.002 PHQ-9 Negative (ref.) Positive 1.60 (1.11, 2.33) 0.013 GAD-7 Negative (ref.) Positive 1.69 (1.16, 2.45) 0.006 Abbreviations: CI: confidence interval; ref: reference category. Participants: n = 943 (1 st Variable), n = 946 (2 nd Variable), n = 946 (3 rd Variable), n = 944 (4 th Variable) Among doctors who screened positive on the AQ-10, over half scored positive for depressive symptoms on the PHQ-9 (53%), anxiety on the GAD-7 (51%), and ADHD traits on the ASRS-6 (57%) (Table 3). Additionally, 27% of AQ-10 positive doctors scored positive on the CORE-10, indicating higher levels of general psychological distress. In contrast, among doctors who screened AQ-10 negative, substantially lower proportions scored positive for PHQ-9 (41%), GAD-7 (38%), CORE-10 (16%), and ASRS (34%). Statistical analysis showed that doctors who screened positive for AQ-10 were significantly more likely to score high for GAD-7 (OR=1.69, CI=(1.16, 2.45) p=0.006), PHQ-9 (OR=1.60, CI=(1.11, 2.33), p=0.013), and CORE-10 (OR=1.99, CI=(1.28, 3.03), p=0.002) and screen positive for ASRS (OR=2.53, p<0.0001) (Table 2). Table 3: AQ-10 Screening grouped by PHQ-9, Core-10, GAD-7, and ASRS Scores PHQ-9 GAD-7 CORE-10 ASRS-6 ≥15 ≤14 ≥15 ≤14 ≥25 ≤24 ≥4 ≤3 Positive (%) 68 (53) 61 (47) 66 (51) 63 (49) 35 (27) 94 (73) 73 (57) 56 (43) Negative (%) 335 (41) 482 (59) 313 (38) 504 (62) 128 (16) 688 (84) 278 (34) 539 (66) Sub-analysis of AQ-10 item-level responses (Table 4) showed that a higher percentage of doctors who screened positive selected positive responses in the domains of Attention to Detail (82%) and Attention Switching (83%) compared to lower positive response rates in Communication (67%), Imagination (55%), and Social Skills (75%) domains. When stratified by medical specialty, psychiatry and surgical specialities showed a similar distribution, with fewer responses that align with autistic traits in Communication, Imagination, and Social Skills in doctors who screen positive on the AQ-10 (Supplementary Tables 1a and 1b). Table 4: Analysis of Responses to AQ-10 items Domain AQ-10 Item Sample Group Screened Positive for Domain Attention to Detail 1. I often notice small sounds when others do not 2. I usually concentrate more on the whole picture, rather than the small details All Doctors 45% AQ-10 Positive 82% Attention Switching 3. I find it easy to do more than one thing at once 4. If there is an interruption, I can switch back to what I was doing very quickly All Doctors 42% AQ-10 Positive 83% Communication 5. I find it easy to ‘read between the lines’ when someone is talking to me 6. I know how to tell if someone listening to me is getting bored All Doctors 17% AQ-10 Positive 67% Imagination 7. When I’m reading a story, I find it difficult to work out the characters’ intentions 8. I like to collect information about categories of things (e.g. types of car, types of bird, types of train, types of plant etc) All Doctors 14% AQ-10 Positive 55% Social 9. I find it easy to work out what someone is thinking or feeling just by looking at their face 10. I find it difficult to work out people’s intentions All Doctors 19% AQ-10 Positive 75% Discussion This large cohort study revealed novel insights into the prevalence and patterns of autistic traits among UK doctors accessing mental health support, presenting new data on demographic and specialty-related factors associated with autistic traits, alongside co-occurring mental health and neurodevelopmental conditions by exploring associations between autistic traits, psychiatric burden, and occupational characteristics. While the estimated prevalence of autism in the UK adult population is 1.1% (15), and comparable rates have been reported among doctors (e.g., 1% of GPs in a UK-based survey; Unigwe et al., 2017),this study identified a markedly higher AQ-10 positive screening rate of 13.6%. Given that the sample comprises doctors actively seeking psychological support, the elevated rate may reflect the disproportionately high burden of mental health difficulties often experienced by autistic individuals (29). The broader issue of under recognition and underdiagnosis of autism in adults may also be more important particularly among healthcare professionals, who may have developed compensatory strategies to mask traits (21,30,31). Compared to female doctors, male doctors in this study were significantly more likely to screen positive for autistic traits (OR = 1.68, p = 0.008). While this may align with the widely reported higher male-to-female diagnostic ratio in the general population (32), it also raises important questions about the potential under recognition of autism in women. Emerging evidence suggests that diagnostic criteria and screening tools may be inadequate in capturing autism in women, contributing to underdiagnosis (33,34). Within a professional context such as medicine, the use of compensatory strategies such as masking or camouflaging autistic traits may be particularly pronounced among women. This can delay recognition and diagnosis and may lead to mental health difficulties before individuals seek support. Therefore, the gender disparity observed in this sample may reflect both true prevalence differences and delayed diagnosis of autism in women. Doctors specialising in psychiatry and surgery showed significantly higher odds of screening positive for autistic traits than general practitioners, potentially highlighting the complex interplay between neurodivergence and the demands of these specialities. Sample sizes may be too small to be certain about this significant association, and further studies are needed to examine associations between autistic traits and medical specialities. Nevertheless, it can be hypothesised that certain medical specialties often require high levels of structured thinking, sustained focus, and high attention to detail, which may be beneficial for doctors in those specialties, as they are qualities that autistic individuals may naturally possess (30,35,36). A recent survey of autistic doctors found that approximately three-quarters experienced executive functioning challenges in their work (18). These factors may influence the self-selection of specialities. These findings highlight the importance of understanding how neurodivergence interacts with professional role demands, and how this may shape both career trajectories and workplace experiences for autistic doctors. Doctors who screened positive on the AQ-10 also tended to screen positive on measures of anxiety, depression, and general psychological distress, indicating a greater psychiatric symptom burden. Importantly, none of the individuals in this sample had a recorded diagnosis of autism, suggesting that undiagnosed autistic traits may interact with mental health conditions in this population. This association may also highlight the psychological strain of navigating clinical environments without recognition or support for neurodivergent differences, reinforcing the importance of earlier identification and tailored interventions. Similarly, an increased rate of screening positive (57%) for ADHD was observed among those who screened positive for autism. This figure is notably higher than the 34% of doctors screening positive for ADHD reported in a previous study. These findings suggest that doctors who are positive for autistic traits may also be likely to have ADHD traits in this cohort, and this aligns with findings from studies showing that 50 to 70% of individuals with autism also present with comorbid ADHD. A greater proportion of doctors who screened positive on the AQ-10 responded positively to Attention to Detail (82%) and Attention Switching (83%) domains, compared to lower positive response rates in Communication (67%), Imagination (55%), and Social Skills (75%). This contrasts with response distributions in the original AQ-50 validation, where positive responses of the same AQ-10 items were more evenly spread across domains: Attention to Detail (74%), Attention Switching (70%), Communication (74%), Imagination (71%), and Social Skills (79%) (37). The prominence of attention-related traits may reflect the cognitive demands of medical practice, where meticulous attention to detail and sustained focus are needed. Frequent task switching is a routine and necessary part of most medical specialties. However, this demand may be especially burdensome for autistic doctors, for whom rapid attention switching can be cognitively taxing and emotionally draining, potentially contributing to psychological distress and help-seeking. Doctors may mask difficulties in social and communicative domains due to the high interpersonal expectations from their roles, resulting in these traits being underreported or unnoticed. Such masking may delay recognition of neurodivergent differences while simultaneously contributing to psychological strain. It is plausible that medicine self-selects for characteristics common in autism, such as pattern recognition, sustained focus, and a conscientious work ethic, traits that may both aid professional performance and obscure underlying neurodivergence (16,17). Limitations and strengths Although the AQ-10 has demonstrated moderate to strong sensitivity (0.88), specificity (0.91), and positive predictive value (0.85) for the diagnosis of autism with a cut-off score of ≥6 (38), it has limitations. The presence of social or generalised anxiety disorders may inflate AQ scores in nonautistic individuals, contributing to false positives (39). The same study also suggested that individuals who score below the threshold may later receive a diagnosis following full clinical assessment, particularly if masking behaviours or concurrent mental illness obscure trait expression. Nonetheless, the tool offers a pragmatic and validated approach in high-throughput settings such as NHS Practitioner Health, where the emphasis is on early detection of potential neurodevelopmental traits in high-functioning adults, including those whose presentations may be complicated by cooccurring psychological distress or professional masking. The study sample included doctors who accessed NHS Practitioner Health for mental health support and may not be representative of the wider medical workforce. This introduces the potential for self-selection bias, since doctors with certain traits or symptom profiles may be more likely to seek help. As such, the findings do not estimate the prevalence of autism among all doctors, but rather the prevalence of autistic traits among those seeking mental health care. The study design is cross-sectional, limiting the ability to infer causality between autistic traits and cooccurring mental health difficulties. However, this study draws on a large, real-world sample of doctors accessing NHS Practitioner Health, a national and high-throughput mental health service for medical professionals in England. This study uses standardised and validated screening instruments, including the AQ-10, PHQ-9, GAD-7, CORE-10, and ASRS, which increase the reliability and comparability of the findings. The inclusion of an item-level sub analysis of the AQ-10 adds further depth to the characterisation of autistic traits in this population. Implications and future directions These findings highlight the clinical importance of screening not only for mental health symptoms but also for underlying autistic traits in doctors accessing mental health support. Understanding underlying neurodevelopmental conditions will help to tailor support and interventions for doctors presenting with mental health problems. More broadly, these findings may have relevance across other cognitively demanding professions where neurodevelopmental conditions may similarly remain undetected and unsupported. Abbreviations ADHD – Attention-Deficit/Hyperactivity Disorder AQ-10 – Autism Spectrum Quotient (10-item screening tool) ASRS – Adult ADHD Self-Report Scale CI – Confidence Interval CORE-10 – Clinical Outcomes in Routine Evaluation (10-item) GP – General Practitioner ICMJE – International Committee of Medical Journal Editors NHS – National Health Service NICE – National Institute for Health and Care Excellence OR – Odds Ratio PH – Practitioner Health (NHS service) PHQ-9 – Patient Health Questionnaire (9-item depression scale) RCGP – Royal College of General Practitioners UK – United Kingdom Declarations Clinical trial number: not applicable. Ethics approval and consent to participate This study and its procedures comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Data were managed under NHS Research Ethics Committee approvals (ref. 24/SW/0019), and the analysed data are only from those who provided informed consent for their data being used for research purposes. Consent for publication Not Applicable Availability of data and materials The data that support the findings of this study are available upon reasonable request. Access to the data can be applied for by contacting NHS Practitioner Health. Competing interests Bhathika Perera has received honoraria for presenting at conferences organised by pharmaceutical companies. Liren Abeyratne, Ken Courtenay, Zaid Al-Najjar, Sebastian C K Shaw, Vincent Odiaka, and Mary Doherty declare no conflicts of interest. Funding This research received no specific grant from any funding agency, commercial or not-for-profit sector. Authors' contributions LA conducted the preliminary data analysis and led the writing of the manuscript. 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Autistic doctors: overlooked assets to medicine. The Lancet Psychiatry. Volume 7. Elsevier Ltd; 2020. pp. 306–7. Miller D, Rees J, Pearson A. Masking Is Life: Experiences of Masking in Autistic and Nonautistic Adults. Autism Adulthood [Internet]. 2021 Dec 1 [cited 2025 Jul 15];3(4):330. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8992921/ Yau N, Anderson S, Smith IC. How is psychological wellbeing experienced by autistic women? Challenges and protective factors: A meta-synthesis. Res Autism Spectr Disord. 2023;102:102101. Kroenke K, Spitzer RL, Williams JBW. The PHQ-9: Validity of a Brief Depression Severity Measure. J Gen Intern Med [Internet]. 2001 [cited 2025 Jul 15];16(9):606. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC1495268/ Barkham M, Bewick B, Mullin T, Gilbody S, Connell J, Cahill J et al. The CORE-10: A short measure of psychological distress for routine use in the psychological therapies. Couns Psychother Res [Internet]. 2013 Mar 1 [cited 2025 Jul 15];13(1):3–13. Available from: https://onlinelibrary.wiley.com/doi/full/ 10.1080/14733145.2012.729069 Brevik EJ, Lundervold AJ, Haavik J, Posserud MB. Validity and accuracy of the Adult Attention-Deficit/Hyperactivity Disorder (ADHD) Self-Report Scale (ASRS) and the Wender Utah Rating Scale (WURS) symptom checklists in discriminating between adults with and without ADHD. Brain Behav [Internet]. 2020 Jun 1 [cited 2025 Jul 15];10(6):e01605. Available from: https://onlinelibrary.wiley.com/doi/full/ 10.1002/brb3.1605 Kessler RC, Adler LA, Gruber MJ, Sarawate CA, Spencer T, Van Brunt DL. Validity of the World Health Organization Adult ADHD Self-Report Scale (ASRS) Screener in a representative sample of health plan members. Int J Methods Psychiatr Res [Internet]. 2007 [cited 2025 Jul 15];16(2):52–65. Available from: https://pubmed.ncbi.nlm.nih.gov/17623385/ Kessler RC, Adler L, Ames M, Demler O, Faraone S, Hiripi E, et al. The World Health Organization adult ADHD self-report scale (ASRS): A short screening scale for use in the general population. Psychol Med. 2005;35(2):245–56. Overview. | Autism spectrum disorder in adults: diagnosis and management | Guidance | NICE. Lai MC. Mental health challenges faced by autistic people. Nature Human Behaviour 2023 7:10 [Internet]. 2023 Oct 20 [cited 2025 Jul 21];7(10):1620–37. Available from: https://www.nature.com/articles/s41562-023-01718-2 Doherty M, Chown N, Martin N, Shaw SCK. Autistic psychiatrists’ experiences of recognising themselves and others as autistic: a qualitative study. BJPsych Open. 2024;10(6). O’Nions E, Petersen I, Buckman JEJ, Charlton R, Cooper C, Corbett A et al. Autism in England: assessing underdiagnosis in a population-based cohort study of prospectively collected primary care data. The Lancet regional health Europe [Internet]. 2023 Jun 1 [cited 2025 Jul 21];29. Available from: https://pubmed.ncbi.nlm.nih.gov/37090088/ Loomes R, Hull L, Mandy WPL. What Is the Male-to-Female Ratio in Autism Spectrum Disorder? A Systematic Review and Meta-Analysis. J Am Acad Child Adolesc Psychiatry [Internet]. 2017 Jun 1 [cited 2025 Jul 21];56(6):466–74. Available from: https://pubmed.ncbi.nlm.nih.gov/28545751/ Alaghband-rad J, Hajikarim-Hamedani A, Motamed M. Camouflage and masking behavior in adult autism. Front Psychiatry. 2023;14:1108110. McCrossin R. Finding the True Number of Females with Autistic Spectrum Disorder by Estimating the Biases in Initial Recognition and Clinical Diagnosis. Children [Internet]. 2022 Feb 1 [cited 2025 Jul 21];9(2):272. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8870038/ Baron-Cohen S, Ashwin E, Ashwin C, Tavassoli T, Chakrabarti B. Talent in autism: hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philosophical Transactions of the Royal Society B: Biological Sciences [Internet]. 2009 [cited 2025 Jul 21];364(1522):1377. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC2677592/ Happé F, Frith U. The weak coherence account: detail-focused cognitive style in autism spectrum disorders. J Autism Dev Disord [Internet]. 2006 Jan [cited 2025 Jul 21];36(1):5–25. Available from: https://pubmed.ncbi.nlm.nih.gov/16450045/ Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E. The autism-spectrum quotient (AQ): evidence from Asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. J Autism Dev Disord [Internet]. 2001 [cited 2025 Jul 21];31(1):5–17. Available from: https://pubmed.ncbi.nlm.nih.gov/11439754/ Allison C, Auyeung B, Baron-Cohen S. Toward Brief Red Flags for Autism Screening: The Short Autism Spectrum Quotient and the Short Quantitative Checklist in 1,000 Cases and 3,000 Controls. J Am Acad Child Adolesc Psychiatry [Internet]. 2012;51(2):202–212.e7. Available from: https://www.sciencedirect.com/science/article/pii/S0890856711010331 Ashwood KL, Gillan N, Horder J, Hayward H, Woodhouse E, McEwen FS, et al. Predicting the diagnosis of autism in adults using the Autism-Spectrum Quotient (AQ) questionnaire. Psychol Med. 2016;46(12):2595–604. Additional Declarations Competing interest reported. Bhathika Perera has received honoraria for presenting at conferences organised by pharmaceutical companies. Liren Abeyratne, Ken Courtenay, Zaid Al-Najjar, Sebastian C K Shaw, Vincent Odiaka, and Mary Doherty declare no conflicts of interest. Supplementary Files SupplementaryMaterials.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7437542","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":510336102,"identity":"a83dca81-dbcf-4678-9479-187aa0664044","order_by":0,"name":"Liren Abeyratne","email":"","orcid":"","institution":"University College London","correspondingAuthor":false,"prefix":"","firstName":"Liren","middleName":"","lastName":"Abeyratne","suffix":""},{"id":510336104,"identity":"5237368c-3720-4524-81ce-3ffeac5b84ae","order_by":1,"name":"Ken Courtenay","email":"","orcid":"","institution":"NHS England","correspondingAuthor":false,"prefix":"","firstName":"Ken","middleName":"","lastName":"Courtenay","suffix":""},{"id":510336109,"identity":"ccddccd1-19cd-4498-ab92-ac44a12fa646","order_by":2,"name":"Zaid Al-Najjar","email":"","orcid":"","institution":"NHS Practitioner Health","correspondingAuthor":false,"prefix":"","firstName":"Zaid","middleName":"","lastName":"Al-Najjar","suffix":""},{"id":510336110,"identity":"e15d1363-0b6d-43cd-b8c8-4e05d7199003","order_by":3,"name":"Sebastian C K Shaw","email":"","orcid":"","institution":"Brighton and Sussex Medical School","correspondingAuthor":false,"prefix":"","firstName":"Sebastian","middleName":"C K","lastName":"Shaw","suffix":""},{"id":510336111,"identity":"ad3ec2fa-2434-4b0f-9dca-0b4dff19ee97","order_by":4,"name":"Vincent Odiaka","email":"","orcid":"","institution":"NHS Practitioner Health","correspondingAuthor":false,"prefix":"","firstName":"Vincent","middleName":"","lastName":"Odiaka","suffix":""},{"id":510336113,"identity":"7e0638ad-3f6e-4576-8381-e20602906bdf","order_by":5,"name":"Mary Doherty","email":"","orcid":"","institution":"University College Dublin School of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Mary","middleName":"","lastName":"Doherty","suffix":""},{"id":510336115,"identity":"2310ec13-c958-430e-9ae6-c7eec988db00","order_by":6,"name":"Bhathika Perera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzklEQVRIiWNgGAWjYBACAxiDn4GB8QCYxUOsFskGBgYStRgcIFaLOQPvwccVFffkjW8kPzjAUGPHYHDmAH4tlg18yYZnzhQbbruRBrToWDKDwdkGAg47wGMm2diWwLjtRg7QYWwHGAzOE3AYUIv5z8Z/CfabZ4C0/CNOixljY0NC4gYJoBbGtgOEHWbZzJcs2XAsIXnGmWcGBxL7knkkCXnfnL334MeGmgTb/vbkhw8+fLOT4zuTQMBlzMjRkEA4VkCAGDWjYBSMglEwsgEAOspERNFzJ6UAAAAASUVORK5CYII=","orcid":"","institution":"University College London","correspondingAuthor":true,"prefix":"","firstName":"Bhathika","middleName":"","lastName":"Perera","suffix":""}],"badges":[],"createdAt":"2025-08-22 22:08:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7437542/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7437542/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102415519,"identity":"ff7d37c2-85f2-41e2-b860-2bc1e15c5250","added_by":"auto","created_at":"2026-02-11 12:43:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1083897,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7437542/v1/cd0783e2-b99e-42b7-8979-327163709b7a.pdf"},{"id":90937333,"identity":"281c5e14-44a5-49b6-b7fb-c72e5822d077","added_by":"auto","created_at":"2025-09-09 17:21:33","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":21325,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-7437542/v1/c09ef4f0de5c8a076f248ba7.docx"}],"financialInterests":"Competing interest reported. Bhathika Perera has received honoraria for presenting at conferences organised by pharmaceutical companies. Liren Abeyratne, Ken Courtenay, Zaid Al-Najjar, Sebastian C K Shaw, Vincent Odiaka, and Mary Doherty declare no conflicts of interest.","formattedTitle":"Autistic Traits in Doctors: A Cross-sectional Study of Doctors Seeking Mental Health Support","fulltext":[{"header":"Background","content":"\u003cp\u003eMental health difficulties are an increasingly pressing concern for the healthcare workforce, with significant implications for clinician wellbeing, workforce retention, and patient care (1,2). Data from NHS England (March 2025) indicate that 27.5% of all health staff absences are due to anxiety, stress, depression, or other psychiatric conditions (3). Doctors, despite their role as health care professionals, are not immune to these mental health difficulties. It has been suggested that the high-pressure nature of clinical environments places doctors at elevated risk for psychiatric disorders, including depression, anxiety, and occupational burnout (4). In the UK, the prevalence of mental health difficulties among doctors has been reported to range from 17% to 52% (5), with contributing factors including long working hours, emotionally demanding roles, and the residual impact of the COVID-19 pandemic (6,7). These mental health conditions not only diminish personal well-being but also impact workplace performance, contributing to burnout, medical errors, absenteeism from work, and a reduced quality of medical care (1,2).\u003c/p\u003e\n\u003cp\u003eThere is a growing understanding of mental disorders such as anxiety, depression, and burnout among doctors\u0026nbsp;(8,9); however, the understanding of neurodevelopmental conditions such as autism and Attention-Deficit/Hyperactivity Disorder (ADHD) in this population is limited. A recent analysis of ADHD among doctors accessing mental health support revealed that approximately 35% of those doctors screened positive using the Adult ADHD Self-Report Scale (ASRS), a rate substantially higher than the estimated 2–3% prevalence of ADHD in the general adult population (10,11). A recent meta-analysis reported a 40.2% lifetime prevalence of ADHD among autistic people, indicating a significant overlap between these neurotypes (12). This raises the possibility that we may be missing unrecognised autism in some doctors experiencing mental health difficulties. A survey by the Royal College of General Practitioners (RCGP) Clinical Priority Group reported that 1% of general practitioners self-identified as autistic (13), whereas another survey of psychiatrists (14) revealed that 2 (1.2%) of 172 respondents explicitly reported being autistic themselves. These values closely mirror the 1.1% prevalence in the general adult population of England (15). However, the growing number of doctors joining autism-specific support networks and peer communities suggests that many more may be undiagnosed or only recently recognise their neurodivergence (16,17).\u003c/p\u003e\n\u003cp\u003eThere is currently a lack of data on whether the presence of autism contributes to the mental health challenges that doctors face. Considering existing evidence, it can be hypothesised that neurodivergent doctors with conditions such as autism and ADHD may face unique challenges in the workplace (18,19). Differences in social communication, sensory sensitivity, and cognitive processing often require greater mental effort to function in neurotypical environments (20). Masking behaviours can lead to exhaustion, meltdowns, or worsening of co-occurring mental health conditions when coping mechanisms are overwhelmed (21). Communication differences are often dismissed as quirks or overlooked due to compensatory strengths, which can delay recognition and support (22). Therefore, it is crucial to understand the role that being autistic may play among doctors experiencing mental health problems not only to improve the support and retention of neurodivergent doctors but also to foster a more inclusive and healthier medical workforce.\u003c/p\u003e\n\u003cp\u003eThis study aims to\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eAssess the prevalence of autistic traits among doctors with mental health difficulties, using a validated screening tool for ASD (AQ-10). \u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"2\" type=\"1\"\u003e\n \u003cli\u003eEvaluate the association between screening positive for autism and demographic factors as well as medical specialities.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"3\" type=\"1\"\u003e\n \u003cli\u003eExplore co-occurring neurodevelopmental or mental health conditions among doctors screening positive for autism.\u003c/li\u003e\n\u003c/ol\u003e\n\u003col start=\"4\" type=\"1\"\u003e\n \u003cli\u003eCharacterise specific autistic traits most commonly described by doctors seeking mental health support.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003eWe conducted a cross-sectional analysis of self-reported data from doctors who accessed NHS Practitioner Health between April and July 2024. The Autism Spectrum Quotient (AQ-10) screened for autistic traits, alongside validated measures of depression (PHQ-9), anxiety (GAD-7), psychological distress (CORE-10), and ADHD traits (ASRS-6). Logistic regression was used to assess associations with demographic, clinical, and speciality variables.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eMethodology\u003c/p\u003e\n\u003cp\u003eThis was a cross-sectional, observational study. Data were collected from electronic self-registration forms completed by doctors seeking support for mental health difficulties through the NHS Practitioner Health between April 2024 and July 2024.\u003c/p\u003e\n\u003cp\u003eThese data included demographic variables (age, gender identity, ethnicity, and medical specialty) and responses to validated self-report screening instruments for common mental disorders and neurodivergence. Mental health was assessed using the following instruments: the 9-item Patient Health Questionnaire (PHQ-9) for depressive symptoms (23), the 7-item Generalized Anxiety Disorder Scale (GAD-7) for anxiety symptoms\u0026nbsp;(Johnson et al., 2019; L\u0026ouml;we et al., 2008), and the 10-item Clinical Outcomes in Routine Evaluation (CORE-10) for general psychological distress (24). Thresholds for a positive screen were defined as scores \u0026ge;15 on the PHQ-9 and GAD-7 and scores \u0026ge;25 on the CORE-10.\u003c/p\u003e\n\u003cp\u003eADHD symptoms were screened using the 6-item Adult ADHD Self-Report Scale v1.1 (ASRS) (25\u0026ndash;27), with a score of 4 or above being considered positive for ADHD. Autistic traits were assessed and screened for using the 10-item Autism Spectrum Quotient (AQ-10), a brief and widely used screening tool recommended by the National Institute for Health and Care Excellence (NICE) for identifying adults who may benefit from a full diagnostic assessment (28). A score of 6 or more was considered as screening positive for autism. Five domains of AQ-10 (Social Skills, Communication, Attention to Detail, Attention Switching, and Imagination) were analysed against scoring positive for the AQ-10. Participants were included if they completed demographic information and the AQ-10 at registration. Cases with missing AQ-10 data or inaccurate dates of birth were excluded.\u003c/p\u003e\n\u003cp\u003eData analysis\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarise the demographic characteristics and prevalence of positive AQ-10 results. The proportions of doctors meeting the thresholds for positive screens on the PHQ-9, GAD-7, CORE-10, and ASRS were calculated for the AQ-10-positive and negative groups. Logistic regression models were used to estimate odds ratios for screening positive on the AQ-10 in relation to age, gender, ethnicity, and medical specialty as well as screening positive on the PHQ-9, GAD-7, CORE-10 and ASRS. Additional sub analyses have examined the distribution of responses across the AQ-10 items to identify patterns in specific autistic trait domains. Item-level data were also further stratified by medical specialty to explore potential domain-level differences across clinical subgroups. All analyses were conducted via R version 4.5.1 (2025-06-13 ucrt), with a significance threshold of p \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere were 1,037 referrals from doctors who sought support for mental health concerns between April and July 2024 and who provided consent for their data to be used for research. Following the exclusion of entries with invalid date-of-birth records and missing responses to the AQ-10, a total of 946 valid entries were retained for analysis (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Characteristics and AQ-10 Results of Doctors Accessing NHS Practitioner Health (PH)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003eDoctors accessing NHS PH\u003cstrong\u003e\u0026nbsp;(%)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003eAQ-10 Positive (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eAQ-10 Negative (%)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll doctors\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e946 (100)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e129 (14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e817 (86)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 349px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFemale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e649 (69)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e75 (12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e574 (88)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e294 (31)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e53 (18)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e241 (82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNot Listed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e3 (0)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1 (33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e2 (67)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 349px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAge:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e20-29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e154 (16)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e30 (19)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e124(81)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e30-39\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e430 (46)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e50 (12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e380 (88)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e40-49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e245 (26)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e28 (11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e217 (89)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e50-59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e104 (11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e18 (17)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e86 (83)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003e60-69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e13 (1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3 (23)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e10 (77)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 349px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eAsian\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e240 (25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e36 (15)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e204 (85)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eBlack\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e49 (5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e7 (14)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e42 (86)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eWhite\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e546 (58)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e66 (12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e480 (88)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e54 (6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e12 (22)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e42(78)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eNot Listed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e47 (5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e7 (15)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e40 (85)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eMissing\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e10 (1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1 (10)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e9 (90)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 349px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialty:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGeneral Practice\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e423 (45)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e49 (12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e374 (88)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eGeneral Medicine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e106 (11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8 (8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e98(92)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e24 (3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e5 (21)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e19 (79)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePsychiatry\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e53 (5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e13 (25)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e40 (75)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eSurgery\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e61 (6)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e13 (21)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e48 (79)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePaediatrics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e50 (5)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8 (16)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e42 (84)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eEmergency Medicine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e34 (4)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3 (9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e31 (91)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eObstetrics and Gynaecology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e24 (3)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e2 (8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e22 (92)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eRadiology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e9 (1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e3 (33)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6 (67)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eIntensive Care and Anaesthesia\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e66 (7)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e8 (12)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e58 (88)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003ePathology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e9 (1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1 (11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e8 (89)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eOphthalmology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e11 (1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e1 (9)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e10 (91)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 151px;\"\u003e\n \u003cp\u003eFoundation/non specialty\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 198px;\"\u003e\n \u003cp\u003e71 (8)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 122px;\"\u003e\n \u003cp\u003e14 (20)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e57 (80)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*Public Health (n=3) and Occupational medicine (n=2) specialties were removed due to small sample size, which limited statistical validity.\u003c/p\u003e\n\u003cp\u003eThe sample was predominantly female (69%). Doctors aged 30\u0026ndash;39 years accounted for the largest proportion of referrals (46%), with those aged 20\u0026ndash;39 contributing 62%. In contrast, referrals from doctors aged 50 years and above were notably lower, accounting for only 12% of the total sample.\u003c/p\u003e\n\u003cp\u003eIn terms of ethnicity, the majority identified as White (58%), followed by Asian (25%). Other ethnic categories were represented in smaller proportions and were grouped accordingly for statistical analysis. In terms of medical specialities, the largest group accessing mental health support were from doctors in general practice (45%), followed by general medicine (11%).\u003c/p\u003e\n\u003cp\u003eA total of 13.6% (n = 129) of the sample met the threshold for a positive screen for autism on the AQ-10. Positive screens were more prevalent among male doctors (18%) than among female doctors (12%). The highest proportion of positive screens was observed among doctors in the 60\u0026ndash;69 years age group (23%); however,\u0026nbsp;this group comprised a relatively small subsample (n=13). Among the age groups with larger (\u0026gt;100) sample sizes, the highest rates of prevalence were in the 20\u0026ndash;29 years age group (17%), followed by those aged 50\u0026ndash;59 years (12%).\u003c/p\u003e\n\u003cp\u003eLogistic regression analysis revealed that males were significantly more likely to screen positive on the AQ-10 than females (OR=1.68, CI=(1.14, 2.46), p=0.008) (Table 2). Doctors aged 22-29 years were also more likely to screen positive on the AQ-10 than those in the reference group (aged 30-39 years). When analysed by medical specialty, psychiatry (OR=2.48, CI=(1.20, 4.86), p=0.010) and surgical (OR=2.07, CI=(1.01, 4.00), p=0.037) specialties were significantly more likely to screen positive on the AQ-10 compared to the reference group of general practitioners.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Results from logistic regression models for the associations between the AQ10 score and demographic variables, medical specialty, and mental health indicators\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristics\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 27.703%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel 1 (AQ10)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95% CI\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP value\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIntercept\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.10, 0.17)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026lt;2e\u003csup\u003e-16\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003eFemales (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eMales\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.68\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.14, 2.46)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge:\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003e30-39 (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e22-29\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.84\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.11, 3.00)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.016\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e40-49\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.59, 1.59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.938\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e50-59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e1.59\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.86, 2.82)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.121\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e60-69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e2.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.50, 7.75)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.222\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthnicity:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003eWhite (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eAsian\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e1.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.82, 1.98)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.264\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eBlack\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e1.21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.48, 2.65)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.654\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eMixed\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.08\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.00, 4.04)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e1.27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.50, 2.79)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.575\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eMissing\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eP Value\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.04, 4.40)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.841\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026lt;0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSpecialty:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 93.0932%;\"\u003e\n \u003cp\u003eGeneral Practice (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eEmergency Medicine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.74\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.17, 2.17)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.627\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eFoundation/non specialty\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e1.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.94, 3.54)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.061\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eIntensive Care and Anaesthetics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e1.05\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.44, 2.22)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.899\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eGeneral Medicine\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.62\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.27, 1.29)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.234\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eObstetrics and Gynaecology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.69\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.11, 2.45)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.628\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eOphthalmology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.76\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.04, 4.11)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.799\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003ePaediatrics\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e1.45\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.60, 3.13)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.367\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003ePathology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e0.95\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.05, 5.36)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.965\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePsychiatry\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.48\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.20, 4.86)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.010\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eRadiology\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e3.82\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.79, 14.96)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.278\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurgery\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.07\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.01, 4.00)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.037\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eOther\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e2.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e(0.64, 5.26)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e0.184\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026lt;0.0001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eASRS:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eNegative (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.53\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.74, 3.70)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.0001\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCORE-10:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eNegative (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.99\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.28, 3.03)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHQ-9\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eNegative (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.60\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.11, 2.33)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD-7\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003eNegative (ref.)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 21.5155%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 5.9062%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.69\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 12.3749%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(1.16, 2.45)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.4218%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.006\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations:\u003c/em\u003e CI: confidence interval; ref: reference category. Participants: n = 943 (1\u003csup\u003est\u003c/sup\u003e Variable), n = 946 (2\u003csup\u003end\u003c/sup\u003e Variable), n = 946 (3\u003csup\u003erd\u003c/sup\u003e Variable), n = 944 (4\u003csup\u003eth\u003c/sup\u003e Variable)\u003c/p\u003e\n\u003cp\u003eAmong doctors who screened positive on the AQ-10, over half scored positive for depressive symptoms on the PHQ-9 (53%), anxiety on the GAD-7 (51%), and ADHD traits on the ASRS-6 (57%) (Table 3). Additionally, 27% of AQ-10 positive doctors scored positive on the CORE-10, indicating higher levels of general psychological distress. In contrast, among doctors who screened AQ-10 negative, substantially lower proportions scored positive for PHQ-9 (41%), GAD-7 (38%), CORE-10 (16%), and ASRS (34%). Statistical analysis showed that doctors who screened positive for AQ-10 were significantly more likely to score high for GAD-7 (OR=1.69, CI=(1.16, 2.45) p=0.006), PHQ-9 (OR=1.60, CI=(1.11, 2.33), p=0.013), and CORE-10 (OR=1.99, CI=(1.28, 3.03), p=0.002) and screen positive for ASRS (OR=2.53, p\u0026lt;0.0001) (Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3: AQ-10 Screening grouped by PHQ-9, Core-10, GAD-7, and ASRS Scores\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePHQ-9\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGAD-7\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCORE-10\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eASRS-6\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;15\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;14\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;15\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;14\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;25\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;24\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;4\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026le;3\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive (%)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e68 (53)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e61 (47)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e66 (51)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e63 (49)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e35 (27)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e94 (73)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e73 (57)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e56 (43)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative (%)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e335 (41)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e482 (59)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e313 (38)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e504 (62)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e128 (16)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e688 (84)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e278 (34)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e539 (66)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eSub-analysis of AQ-10 item-level responses (Table 4) showed that a higher percentage of doctors who screened positive selected positive responses in the domains of Attention to Detail (82%) and Attention Switching (83%) compared to lower positive response rates in Communication (67%), Imagination (55%), and Social Skills (75%) domains. When stratified by medical specialty, psychiatry and surgical specialities showed a similar distribution, with fewer responses that align with autistic traits in Communication, Imagination, and Social Skills in doctors who screen positive on the AQ-10 (Supplementary Tables 1a and 1b).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4: Analysis of Responses to AQ-10 items\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDomain\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAQ-10 Item\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample Group\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eScreened Positive for Domain\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eAttention to Detail\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e1. I often notice small sounds when others do not\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2. I usually concentrate more on the whole picture, rather than the\u0026nbsp;\u003c/p\u003e\n \u003cp\u003esmall details\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAll Doctors\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e45%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAQ-10 Positive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e82%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eAttention Switching\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e3. I find it easy to do more than one thing at once\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4. If there is an interruption, I can switch back to what I was doing very quickly\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAll Doctors\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e42%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAQ-10 Positive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e83%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eCommunication\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e5. I find it easy to \u0026lsquo;read between the lines\u0026rsquo; when someone is talking to me\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6. \u0026nbsp;I know how to tell if someone listening to me is getting bored\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAll Doctors\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e17%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAQ-10 Positive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e67%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eImagination\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e7. \u0026nbsp;When I\u0026rsquo;m reading a story, I find it difficult to work out the characters\u0026rsquo; intentions\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e8. \u0026nbsp;I like to collect information about categories of things (e.g. types of car, types of bird, types of train, types of plant etc)\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAll Doctors\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e14%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAQ-10 Positive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e55%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003eSocial\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 292px;\"\u003e\n \u003cp\u003e9. \u0026nbsp;I find it easy to work out what someone is thinking or feeling just by looking at their face\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10. \u0026nbsp;I find it difficult to work out people\u0026rsquo;s intentions\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAll Doctors\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e19%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 141px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 106px;\"\u003e\n \u003cp\u003eAQ-10 Positive\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 92px;\"\u003e\n \u003cp\u003e75%\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis large cohort study revealed novel insights into the prevalence and patterns of autistic traits among UK doctors accessing mental health support, presenting new data on demographic and specialty-related factors associated with autistic traits, alongside co-occurring mental health and neurodevelopmental conditions by exploring associations between autistic traits, psychiatric burden, and occupational characteristics. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile the estimated prevalence of autism in the UK adult population is 1.1% (15), and comparable rates have been reported among doctors (e.g., 1% of GPs in a UK-based survey; Unigwe et al., 2017),this study identified a markedly higher AQ-10 positive screening rate of 13.6%. Given that the sample comprises doctors actively seeking psychological support, the elevated rate may reflect the disproportionately high burden of mental health difficulties often experienced by autistic individuals (29). The broader issue of under recognition and underdiagnosis of autism in adults may also be more important particularly among healthcare professionals, who may have developed compensatory strategies to mask traits (21,30,31).\u003c/p\u003e\n\u003cp\u003eCompared to female doctors, male doctors in this study were significantly more likely to screen positive for autistic traits (OR = 1.68, p = 0.008). While this may align with the widely reported higher male-to-female diagnostic ratio in the general population (32), it also raises important questions about the potential under recognition of autism in women. Emerging evidence suggests that diagnostic criteria and screening tools may be inadequate in capturing autism in women, contributing to underdiagnosis (33,34). Within a professional context such as medicine, the use of compensatory strategies such as masking or camouflaging autistic traits may be particularly pronounced among women. This can delay recognition and diagnosis and may lead to mental health difficulties before individuals seek support. Therefore, the gender disparity observed in this sample may reflect both true prevalence differences and delayed diagnosis of autism in women.\u003c/p\u003e\n\u003cp\u003eDoctors specialising in psychiatry and surgery showed significantly higher odds of screening positive for autistic traits than general practitioners, potentially highlighting the complex interplay between neurodivergence and the demands of these specialities. Sample sizes may be too small to be certain about this significant association, and further studies are needed to examine associations between autistic traits and medical specialities. Nevertheless, it can be hypothesised that certain medical specialties often require high levels of structured thinking, sustained focus, and high attention to detail, which may be beneficial for doctors in those specialties, as they are qualities that autistic individuals may naturally possess (30,35,36). A recent survey of autistic doctors found that approximately three-quarters experienced executive functioning challenges in their work (18). These factors may influence the self-selection of specialities. These findings highlight the importance of understanding how neurodivergence interacts with professional role demands, and how this may shape both career trajectories and workplace experiences for autistic doctors.\u003c/p\u003e\n\u003cp\u003eDoctors who screened positive on the AQ-10 also tended to screen positive on measures of anxiety, depression, and general psychological distress, indicating a greater psychiatric symptom burden. Importantly, none of the individuals in this sample had a recorded diagnosis of autism, suggesting that undiagnosed autistic traits may interact with mental health conditions in this population. This association may also highlight the psychological strain of navigating clinical environments without recognition or support for neurodivergent differences, reinforcing the importance of earlier identification and tailored interventions. Similarly, an increased rate of screening positive (57%) for ADHD was observed among those who screened positive for autism. This figure is notably higher than the 34% of doctors screening positive for ADHD reported in a previous study. These findings suggest that doctors who are positive for autistic traits may also be likely to have ADHD traits in this cohort, and this aligns with findings from studies showing\u0026nbsp;that 50 to 70% of individuals with autism also present with comorbid\u0026nbsp;ADHD.\u003c/p\u003e\n\u003cp\u003eA greater proportion of doctors who screened positive on the AQ-10 responded positively to Attention to Detail (82%) and Attention Switching (83%) domains, compared to lower positive response rates in Communication (67%), Imagination (55%), and Social Skills (75%). This contrasts with response distributions in the original AQ-50 validation, where positive responses of the same AQ-10 items were more evenly spread across domains: Attention to Detail (74%), Attention Switching (70%), Communication (74%), Imagination (71%), and Social Skills (79%) (37).\u0026nbsp;The prominence of attention-related traits may reflect the cognitive demands of medical practice, where meticulous attention to detail and sustained focus are needed. Frequent task switching is a routine and necessary part of most medical specialties. However, this demand may be especially burdensome for autistic doctors, for whom rapid attention switching can be cognitively taxing and emotionally draining, potentially contributing to psychological distress and help-seeking. Doctors may mask difficulties in social and communicative domains due to the high interpersonal expectations from their roles, resulting in these traits being underreported or unnoticed. Such masking may delay recognition of neurodivergent differences while simultaneously contributing to psychological strain. It is plausible that medicine self-selects for characteristics common in autism, such as pattern recognition, sustained focus, and a conscientious work ethic, traits that may both aid professional performance and obscure underlying neurodivergence (16,17).\u003c/p\u003e\n\u003cp\u003eLimitations and strengths\u003c/p\u003e\n\u003cp\u003eAlthough the AQ-10 has demonstrated moderate to strong sensitivity (0.88), specificity (0.91), and positive predictive value (0.85) for the diagnosis of autism with a cut-off score of ≥6 (38), it has limitations. The presence of social or generalised anxiety disorders may inflate AQ scores in nonautistic individuals, contributing to false positives (39). The same study also suggested that individuals who score below the threshold may later receive a diagnosis following full clinical assessment, particularly if masking behaviours or concurrent mental illness obscure trait expression. Nonetheless, the tool offers a pragmatic and validated approach in high-throughput settings such as NHS Practitioner Health, where the emphasis is on early detection of potential neurodevelopmental traits in high-functioning adults, including those whose presentations may be complicated by cooccurring psychological distress or professional masking.\u003c/p\u003e\n\u003cp\u003eThe study sample included doctors who accessed NHS Practitioner Health for mental health support and may not be representative of the wider medical workforce. This introduces the potential for self-selection bias, since doctors with certain traits or symptom profiles may be more likely to seek help. As such, the findings do not estimate the prevalence of autism among all doctors, but rather the prevalence of autistic traits among those seeking mental health care. The study design is cross-sectional, limiting the ability to infer causality between autistic traits and cooccurring mental health difficulties.\u003c/p\u003e\n\u003cp\u003eHowever, this study draws on a large, real-world sample of doctors accessing NHS Practitioner Health, a national and high-throughput mental health service for medical professionals in England. This study uses standardised and validated screening instruments, including the AQ-10, PHQ-9, GAD-7, CORE-10, and ASRS, which increase the reliability and comparability of the findings. The inclusion of an item-level sub analysis of the AQ-10 adds further depth to the characterisation of autistic traits in this population.\u003c/p\u003e\n\u003cp\u003eImplications and future directions\u003c/p\u003e\n\u003cp\u003eThese findings highlight the clinical importance of screening not only for mental health symptoms but also for underlying autistic traits in doctors accessing mental health support. Understanding underlying neurodevelopmental conditions will help to tailor support and interventions for doctors presenting with mental health problems. More broadly, these findings may have relevance across other cognitively demanding professions where neurodevelopmental conditions may similarly remain undetected and unsupported.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eADHD \u0026ndash; Attention-Deficit/Hyperactivity Disorder\u003c/p\u003e\n\u003cp\u003eAQ-10 \u0026ndash; Autism Spectrum Quotient (10-item screening tool)\u003c/p\u003e\n\u003cp\u003eASRS \u0026ndash; Adult ADHD Self-Report Scale\u003c/p\u003e\n\u003cp\u003eCI \u0026ndash; Confidence Interval\u003c/p\u003e\n\u003cp\u003eCORE-10 \u0026ndash; Clinical Outcomes in Routine Evaluation (10-item)\u003c/p\u003e\n\u003cp\u003eGP \u0026ndash; General Practitioner\u003c/p\u003e\n\u003cp\u003eICMJE \u0026ndash; International Committee of Medical Journal Editors\u003c/p\u003e\n\u003cp\u003eNHS \u0026ndash; National Health Service\u003c/p\u003e\n\u003cp\u003eNICE \u0026ndash; National Institute for Health and Care Excellence\u003c/p\u003e\n\u003cp\u003eOR \u0026ndash; Odds Ratio\u003c/p\u003e\n\u003cp\u003ePH \u0026ndash; Practitioner Health (NHS service)\u003c/p\u003e\n\u003cp\u003ePHQ-9 \u0026ndash; Patient Health Questionnaire (9-item depression scale)\u003c/p\u003e\n\u003cp\u003eRCGP \u0026ndash; Royal College of General Practitioners\u003c/p\u003e\n\u003cp\u003eUK \u0026ndash; United Kingdom\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eClinical trial number: not applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study and its procedures comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008. Data were managed under NHS Research Ethics Committee approvals (ref. 24/SW/0019), and the analysed data are only from those who provided informed consent for their data being used for research purposes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available upon reasonable request. Access to the data can be applied for by contacting NHS Practitioner Health.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBhathika Perera has received honoraria for presenting at conferences organised by pharmaceutical companies. Liren Abeyratne, Ken Courtenay, Zaid Al-Najjar, Sebastian C K Shaw, Vincent Odiaka, and Mary Doherty declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency, commercial or not-for-profit sector.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLA conducted the preliminary data analysis and led the writing of the manuscript. BP led the study design, supervised the project, and contributed to data analysis and interpretation. VO performed the statistical analyses. KC, ZA, SS, and MD contributed to refining the research questions and provided critical input during the drafting and revision of the manuscript. All authors reviewed and approved the final version of the manuscript and meet the ICMJE criteria for authorship.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Louisa Dallmeyer and the team at NHS Practitioner Health.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDe Hert S. Burnout in healthcare workers: Prevalence, impact and preventative strategies. Vol. 13, Local and Regional Anesthesia. Dove Medical Press Ltd; 2020. pp. 171\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShanafelt TD, Bradley KA, Wipf JE, Back AL. Burnout and self-reported patient care in an internal medicine residency program. Ann Intern Med. 2002;136(5):358\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNHS Sickness Absence Rates. January 2024 - NHS England Digital [Internet]. [cited 2025 Jul 16]. 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Psychol Med. 2016;46(12):2595\u0026ndash;604.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Autism, Doctors, Mental health, Neurodiversity","lastPublishedDoi":"10.21203/rs.3.rs-7437542/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7437542/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNeurodevelopmental conditions are increasingly recognised as important to understanding mental health difficulties. However, the presence of autism among doctors experiencing mental health problems remains underexplored. This study aims to estimate the prevalence of autistic traits among doctors seeking mental health support and examine associations with demographic characteristics, medical speciality, and co-occurring mental health and neurodevelopmental conditions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a cross-sectional analysis of self-reported data from doctors who accessed NHS Practitioner Health between April and July 2024. The Autism Spectrum Quotient (AQ-10) screened for autistic traits, alongside validated measures of depression (PHQ-9), anxiety (GAD-7), psychological distress (CORE-10), and ADHD traits (ASRS-6). Logistic regression was used to assess associations with demographic, clinical, and speciality variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf the 946 doctors included, 13.6% screened positive for autism using the AQ-10. Positive screening was significantly associated with male gender (OR = 1.68, p = 0.008), age 22–29 (OR = 1.84, p = 0.016), working in psychiatry (OR = 2.48, p = 0.010), and surgical specialities (OR = 2.07, p = 0.037). AQ-10 positivity was also associated with significantly higher odds of anxiety, depression, psychological distress, and screening positive for ADHD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDiscussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study found a notably high prevalence of autistic traits among UK doctors seeking mental health support which raises the possibilities of under-recognition and possible masking within this workforce. Associations with gender, specialty, and co-occurring psychiatric symptoms highlight the need for earlier identification and tailored support systems. Findings are limited by reliance on self-report screening and a restricted sample of doctors actively seeking psychological support, limiting generalisability. Nevertheless, this study provides novel insights into neurodiversity in medicine.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA substantial proportion of doctors seeking mental health support screened positive for autistic traits with notable psychiatric and other neurodevelopmental comorbidity. These findings highlight the importance of considering underlying autism when assessing and supporting doctors with mental health difficulties.\u003c/p\u003e","manuscriptTitle":"Autistic Traits in Doctors: A Cross-sectional Study of Doctors Seeking Mental Health Support","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-09 17:21:28","doi":"10.21203/rs.3.rs-7437542/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"8e9cb9a8-a047-4ece-afdf-a08cbd0b926c","owner":[],"postedDate":"September 9th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-11T12:42:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-09 17:21:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7437542","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7437542","identity":"rs-7437542","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}