Localization of the Autism Spectrum Disorder Knowledge Scale Professional Version (ASKSP-R) in Western Cities of China: A Case Study

Localization of the Autism Spectrum Disorder Knowledge Scale Professional Version (ASKSP-R) in Western Cities of China: A Case Study | 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 Localization of the Autism Spectrum Disorder Knowledge Scale Professional Version (ASKSP-R) in Western Cities of China: A Case Study Yueying Zhang, Zhujun Zhao, Fang Hou, Yang Hong, Feng Hong, Fudong Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6184251/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 Objective This study aimed to translate, revise, and validate the Autism Spectrum Knowledge Scale for Professional Version-Revised (ASKSP-R), which is used to assess the knowledge of professionals involved in autism spectrum disorder (ASD) care and services, such as clinicians, educators, and therapists, in the Chinese context. Methods We used random and snowball sampling methods; the scale was revised via methods such as expert consultation and a literature search. We used EXCEL for descriptive analyses, SPSS27.0 for assessing the scale’s reliability and validity, and AMOS26 for validated factor analysis. Moreover, the 2PL model in item–response theory (IRT) was analyzed for discrimination ability via R Studio (version 4.2.2). Results With a Cronbach’s α of 0.885, the internal consistency of the ASKSP-R (Chinese version) was good. Moreover, a KMO of 0.888 indicated the scale’s superior validity, while its multidimensional structure was illustrated by RMSEA = 0.059, IFI = 0.88, TLI = 0.866, and RFI = 0.853. All the entries were between > 0.5 and 0.36 and > 0.66, respectively. Conclusions The revised ASKSP-R scale has robust psychometric properties and can be used to assess the relevant knowledge of clinicians, educators, therapists, and other ASD-related professionals, enabling appropriate interventions on the basis of assessment results, promoting targeted training and education, and increasing the rate of early diagnosis and intervention for ASD. Autism Spectrum Disorder Knowledge Autism Spectrum Knowledge Scale for Professional Version-Revised scale Chinese Professional population Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Autism spectrum disorder (ASD) was first proposed by Kanner ( 1 ). The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5 ™ ), specifies persistent deficits in social communication and interaction, as well as restricted and repetitive behaviors as the main characteristics of ASD ( 2 ). Recently, the global prevalence of ASD has increased significantly, and approximately 1% of children are diagnosed with ASD ( 3 ). Over 10 million people in China have been diagnosed with ASD, including approximately 2 million children aged younger than 12 years. The number of new ASD cases is increasing at a rate of > 100,000 additional children per year ( 4 ). Despite the increasing prevalence of ASD, the issue of delayed diagnosis persists. While the average age at diagnosis is 4–5 years, the majority of ASD children do not undergo developmental assessment before the age of 3 years ( 5 – 9 ). Delayed diagnosis prevents young children from benefiting from their optimal neuroplasticity period. In addition to missing out on the ideal time to intervene, it also increases the burden on both individuals and society ( 10 ). Hence, early diagnosis and interventions have significantly improved the prognosis of ASD children and reduced long-term social expenditure ( 11 – 13 ). Moreover, a lack of expertise in relevant professionals can cause misdiagnosis or delayed diagnosis and slow the patient’s recovery process ( 14 – 16 ). However, ASD knowledge in the general Chinese population is low. Although the level of ASD knowledge among medical professionals is particularly important, there is currently no assessment tool for understanding the level of ASD knowledge among Chinese professionals ( 17 ). Thus, a comprehensive assessment tool is needed to understand and improve ASD knowledge among professionals. In the Chinese context, ASD diagnosis and intervention involve a multidisciplinary approach. Screening mainly begins with pediatricians during routine health checkups, followed by referrals to psychologists or neurologists for further evaluation. Educational and rehabilitation professionals, such as special education teachers, are essential in implementing interventions tailored to the child's needs. Although formal prequalification training in ASD is not universally required for all professionals, psychologists and medical practitioners typically receive some training as part of their professional education. However, the depth of this training varies widely, and additional professional development is often needed to ensure adequate knowledge and skills for ASD-related tasks. In recent years, domestic studies on ASD knowledge have focused mainly on the general population. Few studies have targeted ASD professionals, and most existing questionnaires are self-developed questionnaires ( 18 ) and lack a scientific basis. A few translated foreign scales are available for the general population ( 19 ). Very few ASD knowledge scales have been developed for relevant professionals. The general Chinese population has limited awareness of ASD (Yu et al., 2020), with the majority unaware that effective interventions and support strategies are available for individuals with ASD (Su et al., 2023). Furthermore, many people are unaware of where skill-building interventions or specialized training programs for ASD are most effectively implemented ( 20 ). A study by Huang et al. (2012) indicated that approximately 80% of parents choose “wait and see” if their child develops abnormal behaviors as they grow older. Sullivan et al. (2023) suggested that the lowest level of ASD knowledge in the general population was in the area of ASD etiology. However, Lu et al. (2022) revealed that the general population is aware that children should be taken to the hospital after they develop abnormalities. A majority of them also consider that ASD requires long-term skill-building interventions or specialized training programs and that symptoms can be improved by long-term training. Presently, the questionnaire used to measure ASD knowledge in the general Chinese population is the Autism Stigma and Knowledge Questionnaire (ASK-Q), which was modified by Lodi et al. This questionnaire has been widely used domestically and internationally ( 21 , 22 ). The majority of the general population is unaware of the primary symptoms of ASDs, ASD cooccurring with intellectual disability, and prognosis ( 21 ). Gu et al.’s questionnaire, exclusively for kindergarten healthcare providers, did not include a scale for ASD professionals ( 23 ). Although China has various ASD knowledge questionnaires, most of these questionnaires are aimed at the general population or caregivers, and there is a lack of ASD scales specifically for professionals ( 17 , 19 , 21 , 24 , 25 ). Research on ASD in foreign countries remains in its early stages, with a particular concentration on the knowledge of ASD professionals. Several international questionnaires have been developed to assess ASD knowledge, including the Knowledge about Childhood Autism among Health Workers (KCAHW) scale by Bakare et al. (2008), the Autism Knowledge Scale by Unigwe et al. (2017), the Autism Knowledge Scale by Crane et al. (2019), and the Autism Spectrum Knowledge Scale Professional Version-Revised (ASKSP-R) by McClain et al. (2020). The Autism Knowledge Scale by Unigwe et al. (2017) is a relatively new instrument based on an earlier scale by Zwaigenbaum et al. (2015). However, it provides only “correct” and “incorrect” response options, omitting a “don’t know” choice, which limits its ability to fully capture a doctor’s actual ASD knowledge. While the scale by Crane et al. (2019) is more comprehensive than that of Unigwe et al. (2017), it lacks a rehabilitation component, is more cumbersome to use, and demands greater effort. The KCAHW questionnaire, developed by Bakare et al. (2008), is widely used and frequently referenced. However, it lacks coverage of diagnostic aspects and does not provide sufficient detail for specialized medical professionals. In contrast, the ASKSP-R scale is praised for its scientific rigor and comprehensiveness, although no Chinese version is currently available. The ASKSP-R scale for ASD professionals, developed by McClain et al. (2024), provides comprehensive knowledge coverage, demonstrates strong reliability and validity, and aligns with the DSM-5 by addressing multiple disease content areas. To reduce guesswork, the scale includes a “do not know” response option alongside the traditional “correct” and “incorrect” options (McMahon et al., 2024). However, a Chinese version of the scale has not yet been developed (McClain et al., 2020). Consequently, the scale has been translated for this purpose. While there is no cure for autism, evidence-based interventions, such as behavioral therapies, educational support, and targeted skill-building strategies, can significantly improve the outcomes and quality of life of individuals with ASD. This study aimed to translate and adapt the ASKSP-R scale to assess ASD professional knowledge in China through localized revisions. It also aimed to evaluate the current knowledge levels of Chinese ASD professionals, identify factors influencing their understanding of ASD, and develop targeted training programs to increase their expertise. These efforts are expected to improve early diagnosis and intervention for ASD, thereby enhancing affected patients’ quality of life. 2. Methods 2.1 Scale revision After translation, the scale was revised via the TRAPD (Translation, Review, Adjudication, Pretesting, and Documentation) model, with consent from the original authors (Chow et al., 2021). First, two professional translation teams independently translated and reviewed the scale. A third qualified translator then reviewed the translation, conducted an accuracy check, and completed the reviewer’s prescreening. Additionally, the scale was modified through expert consultations and a literature review. Two senior ASD experts, with extensive experience in both research and clinical practice, were consulted to refine the questionnaire content. Their input was crucial for adapting the scale to the Chinese context and ensuring its localization. A presurvey was conducted to assess the scale’s feasibility and comprehensibility in real-world use. On the basis of the presurvey feedback, several questions were revised to increase the scale’s clarity and applicability. The revised version was again reviewed and confirmed by the ASD experts, resulting in the final Chinese version of the ASKSP-R scale. On the basis of the experts’ comments and relevant policy literature, modifications were made to the ASKSP-R scale, which are listed in the Supplementary File ‘Chinese version of ASKSP-R(EN-revised)’. 2.2 Study subjects The study participants included physicians (e.g., pediatricians, neurologists) responsible for ASD screening and diagnosis and special education teachers involved in intervention and rehabilitation. The study aimed to include a mix of professionals from both specialist and general settings, such as hospitals and special education schools, to capture a wide range of expertise. However, professionals in specialist schools may work predominantly with children with more severe needs, which could influence their perspectives and knowledge. Future research could consider stratified sampling to better represent different professional subgroups and settings. Professionals involved in ASD diagnosis and care in China include psychologists, who provide assessments and initial diagnoses; medical practitioners (e.g., pediatricians, neurologists, child health doctors), who are responsible for confirming diagnoses and developing intervention plans tailored to the individual’s needs and cooccurring conditions; and special education teachers, who contribute to intervention and rehabilitation programs. The scale was based on random and snowball sampling methods. A total of 2,550 questionnaires were distributed, and 2548 valid questionnaires were returned, for a recovery rate of 99.92%. Our study population comprised teachers ≥ 18 years old, those working in special education schools, and physicians directly involved in ASD screening, diagnosis, and intervention. Those with incomplete questionnaires or who did not provide informed consent were excluded to ensure the accuracy and reliability of the data. Table 1 lists the participants’ demographic information, including age, sex, education, title, ethnicity, whether they have interacted with ASD children, whether they know about ASD, and how they learned about it. Table 1 shows the patients’ demographic information. Table 1 Patients’demographic data Considerations Groups Number of participants N = 2548(%) (a person’s) age 18–25 years 202(7.93) 26–35 years 1462(57.38) 36–45 years 664(26.06) 46–55 years 192(7.54) 55–60 years 25(0.98) 60 years and over 3(0.12) distinguishing between the sexes male 679(26.65) female 1869(73.35) academic qualifications Technical secondary school and below 6(0.24) College/Undergraduate 2434(95.53) Postgraduate and above 108(4.24) title none 302(11.85) Primary title 1164(45.68) Middle title 810(31.79) Vice-senior title 206(8.08) Senior title 66(2.59) nation Han ethnic group 1491(58.52) Miao ethnic group 313(12.28) Tujia ethnic group 136(5.33) Dong ethnic group 147(5.76) Buyi ethnic group 174(6.82) the rest 287(11.24) Exposure to children with ASD Yes 1190(46.70) No 1358(53.30) Whether you have learnt about ASD Yes 1015(39.84) No 1533(60.16) In what way do you learn about ASD Self-study 625(24.53) I learnt it in school. 464(18.20) Unified on-site training from company 379(14.86) Unified online training from company 322(12.62) the rest 152(5.96) 2.3 Data collection and procedures We collected data from April 2024 to May 2024. The specific steps were as follows: presurvey period: A presurvey was conducted from 13 to 18 February 2024 with 32 participants, including a mix of medical professionals and special education teachers, to evaluate the feasibility, clarity, and cultural relevance of the translated scale. Participant feedback from the presurvey highlighted areas where question phrasing needed to be simplified or localized. These changes were essential to ensure that the scale was understandable and relevant to Chinese professionals. The finalized scale was further tested for consistency and clarity before full-scale data collection. Feedback from this phase identified ambiguities in certain questions and informed adjustments to improve clarity and applicability. For example, terminology inconsistencies and region-specific content were refined to better align with Chinese professionals’ practices. These modifications were reviewed and approved by two senior ASD experts. Preparation period: We contacted hospital directors to ensure precise questionnaire distribution and data collection. In terms of the questionnaire distribution, the Chinese version of the ASKSP-R scale was distributed to ASD-related departments such as pediatrics, rehabilitation, neurology, psychiatry, and schools for special children after the consent of the head of the hospital was obtained. The head of each department and school was responsible for administering the questionnaire and explaining it to the participants. Before starting the survey, the questionnaire mentioned the study’s purpose, estimated completion time, and content of the informed consent form. The participants completed the questionnaire voluntarily to ensure their right to information and willingness to participate. Data were collected via random sampling and snowball sampling. First, several hospitals were randomly selected for our survey. The questionnaire’s QR code was subsequently sent to the interviewed doctors and special education teachers through the Questionnaire Star platform. This was further shared with other professionals for a snowball effect and to expand the sample coverage. Considering the extensive geographical distribution of the participating hospitals and the workload of the target population, we adopted an electronic questionnaire format and unified data collection through the Questionnaire Star platform ( https://www.wjx.cn/login.aspx ). The Questionnaire Star platform is a free online questionnaire survey and evaluation platform that includes an efficient and convenient online questionnaire design and data collection method and is widely used in several Chinese studies. With this platform, researchers can design questionnaires online and independently, with the integration of questionnaire quantity, time, and location. Because it is more efficient and convenient, it is widely used in China. Before beginning the survey, the heads of the participating hospitals and schools were responsible for facilitating the distribution of the questionnaires and providing general information to the participants. However, the consent process was strictly individual. The participants were provided with detailed information about the study’s purpose, the estimated time commitment, and the content of the informed consent form. They were then asked to provide informed consent voluntarily before completing the questionnaire. This ensured that the administration by the heads of facilities did not influence the autonomy of participants’ decisions to participate. All participants answered a few social and demographic questions and completed the ASKSP-R scale and the ASK-Q questionnaire. 2.4 Ethics Our study was approved by the Ethics Committee of Guizhou Nursing Vocational College. Our approval number was gzhlllscb2024-0301. All participants signed an informed consent form before participation and had the option of withdrawing at any time. The participants were not harmed physically or psychologically throughout the study. The participants’ privacy was strictly protected, all personal information (e.g., name, education, etc.) was anonymized, and raw data access was restricted to authorized personnel only. Additionally, our results were presented in a summary without revealing participants’ personal information and were used for academic research only. The role of the facility heads was strictly logistical, ensuring that participants were informed about the study, but the consent process was conducted independently of each participant. The heads did not influence the participants' decision to consent, which ensured the voluntary and informed nature of their participation. 2.5 Data analysis 2.5.1 Descriptive analysis. The sample’s demographic information was analyzed via descriptive statistics via EXCEL. Descriptive statistics included frequencies, percentages, means, and standard deviations. 2.5.2. Reliability analysis. To assess the questionnaire’s internal consistency, we analyzed the reliability of the sample and questionnaire dimensions via SPSS 27.0 and Cronbach's α, respectively. Cronbach's α coefficient is frequently used in reliability analyses of measurement instruments and can be used to assess a questionnaire’s internal consistency precisely. 2.5.3 Structural validity. For the structural validity analysis, we first assessed the structural validity of the ASKSP-R scale (Chinese version) via confirmatory factor analysis (CFA). AMOS26 software was subsequently used to conduct validated factor analysis on the ASKSP-R scale (Chinese version). The analysis metrics included parameters such as the chi-square test (χ2), degrees of freedom (df), comparative fit index (CFI), Tucker Lewis Index (TLI), and root mean square error of approximation (RMSEA). The scale’s validity was subsequently consolidated by calculating convergent (CR values) and discriminant (AVE and its square root) validities. 2.5.4 Item response theory. Item response theory (IRT) is a statistical method for analyzing test questions and subjects' abilities. There are three commonly used models: the one-parameter logistic model (Rasch model), the two-parameter logistic model (2PL model), and the three-parameter logistic model (3PL model). In this study, the 2PL model was used to analyze the scale items according to the scale dimensions and the model complexity. The differentiation (a) and difficulty (b) parameters of each item were estimated to select the appropriate items. The 2PL model-specific Eq. 1 was as follows: a i is the differentiation parameter of the ith entry. b i is the difficulty parameter of the ith entry. IRT was conducted through R Studio (version 4.2.2). In the 2PL IRT model, the scale was analyzed primarily by difficulty (b) and differentiation (a) parameters, where (b) reflects the difficulty level of the questions and (a) reflects the ability of the question to distinguish between subjects with varying knowledge. A difficulty parameter of + 1 was considered extremely difficult. A differentiation level of 1 denoted high differentiation, with valid questions. The item characteristic curve (ICC) graph represents the association between the subject's ability level (θ) and the probability of answering the question correctly ( p (θ)) as a logistic graph. The estimated total score’s expected value plot also represented the correlation between the subject's ability level (θ) and the subject's total score (T(θ)) in the form of a logistic plot. 2.5.5 Scale validity. The scale’s validity was analyzed via the ASK-Q scale compared with the Chinese version of the ASKSP-R scale. The ASK-Q is available in Chinese and contains three dimensions ( 21 ): ( 1 ) diagnosis/symptoms, ( 2 ) etiology, and ( 3 ) treatment, which are similar to the dimensions of the ASKSP-R scale. The internal consistency of the ASK-Q KR-20 coefficient for the Chinese and US samples was 0.72 and 0.82, respectively, within the acceptable range ( 21 ). Moreover, the ASK-Q is widely used to assess ASD knowledge in the population ( 22 , 26 , 27 ). Since the ASK-Q has been used as the validity scale before ( 28 ), we chose the ASK-Q as the validity measure of the Chinese ASKSP-R scale. For scale validity, we performed linear regression analyses of the ASK-Q and ASKSP-R (Chinese versions) via SPSS 27.0 software. 2.6 Calculation of results To measure knowledge, we coded the ASKSP-R scale’s knowledge portion. Each participant scored 1 for a correct answer, 0 for an incorrect answer, and 0 for a "do not know" answer ( 29 ). Afterward, the scores were classified as low, medium, or high. Scores between 0 and 9 denote a poor level of knowledge, scores between 10 and 18 indicate medium knowledge, and scores between 18 and 25 suggest a higher level of knowledge ( 30 ). 3. Results 3.1 Reliability test As shown in Table 2 , the Cronbach's α of the ASKSP-R scale (text version) was 0.885, indicating that the scale’s internal consistency was high and that the reliability of the ASKSP-R scale (Chinese version) was satisfactory. Table 2 Reliability statistics Cronbach factor Item count (of a consignment etc.) 0.885 25 3.2 Validity test As depicted in Table 3 , the KMO value was 0.888, indicating that the ASKSP-R (Revised Chinese Version) scale is appropriate for factor analysis. Bartlett's test of sphericity yielded a significance value of 0.000, which is less than 0.01, passing the 1% significance level. This further confirms that the ASKSP-R (Revised Chinese Version) scale is appropriate for factor analysis. 3.3 Structural validity 3.3.1 Validation factor analysis. According to the scree plot, the inflection point occurred at 5, suggesting that 4 to 6 factors influence the scale and confirming its multidimensional structure (Fig. 1 ). The overall fit coefficients were as follows: (chi-square degrees of freedom ratio) X 2 /df = 9.862, RMSEA = 0.059 (< 0.08), IFI = 0.88, CFI = 0.88, NFI = 0.868, TLI = 0.866, and RFI = 0.853, which are approximately 0.9 (Table 3 ). These results indicate the model’s good fit, acceptable relationships, and complementarities among the factors and the multidimensional structure of the ASKSP-R scale (Table 4 ). Table 3 KMO and Bartlett's tests of sphericity values KMO Quantity of Sample Suitability Bartlett's test of sphericity approximate chi-square degrees of freedom significance 0.888 10502.562 300 0.000 Table 4 Overall model’s fit coefficients X2/df RMSEA IFI CFI NFI TLI RFI 9.862 0.059 0.880 0.880 0.868 0.866 0.853 The four latent variables, involving etiology and epidemiology, treatment, symptoms and related behaviors, and assessment and diagnosis, demonstrated the highest factor loading (0.716) for item 12 in the "symptoms and related behaviors" dimension and the lowest (0.531) for item 6 in the same dimension. The factor loadings for all the items associated with the four latent variables were greater than 0.5, indicating that these latent variables effectively represented the relevant constructs. The average variance extracted (AVE) for each latent variable and the composite reliability (CR) were both greater than 0.36 and 0.66, respectively, suggesting acceptable convergent validity. These findings support the multidimensional structure of the ASKSP-R scale (Table 5 and Fig. 2 ). Table 5 Factor loads of all dimensions trails Estimate AVE CR 3 <--- Etiology and epidemiology 0.586 0.390 0.792 2 <--- Etiology and epidemiology 0.715 1 <--- Etiology and epidemiology 0.682 4 <--- Etiology and epidemiology 0.570 19 <--- Etiology and epidemiology 0.581 24 <--- Etiology and epidemiology 0.597 11 <--- treatment 0.665 0.394 0.66 10 <--- treatment 0.614 8 <--- treatment 0.601 12 <--- Symptoms and associated behaviors 0.716 0.387 0.79 6 <--- Symptoms and associated behaviors 0.531 5 <--- Symptoms and associated behaviors 0.653 16 <--- Symptoms and associated behaviors 0.634 17 <--- Symptoms and associated behaviors 0.622 20 <--- Symptoms and associated behaviors 0.561 13 <--- Assessment and diagnosis 0.642 0.401 0.87 9 <--- Assessment and diagnosis 0.638 7 <--- Assessment and diagnosis 0.579 14 <--- Assessment and diagnosis 0.66 15 <--- Assessment and diagnosis 0.676 18 <--- Assessment and diagnosis 0.607 21 <--- Assessment and diagnosis 0.654 22 <--- Assessment and diagnosis 0.616 23 <--- Assessment and diagnosis 0.594 25 <--- Assessment and diagnosis 0.656 3.3.2 Item response theory. In the 2PL IRT model, the majority of the questions were of medium difficulty. The model had 13 medium-difficulty items, with questions 3, 6, 7, 9, 13, 14, 15, 18, 19, 20, 21, 22, and 25, with question 7 ("The following professional who can diagnose ASDs is") being the easiest question and was answered correctly by 67% of the participants (η = -0.492). However, 12 questions were more difficult, with questions 1, 2, 4, 5, 8, 10, 11, 12, 16, 17, 23, and 24. The question with the highest difficulty coefficient was question 10 ("Which of the following is not an evidence-based intervention for individuals with ASD?") (η = 2.113, Table 6 and Fig. 4 ). Table 6 Difficulty and differentiation levels of the ASKSP-R (Chinese version) Dimension Title Number Correctness rate(%) Problem Distinctiveness Etiology and epidemiology 1 22 1.053 1.628 2 20 1.159 1.719 3 30 0.837 1.276 4 23 1.195 1.302 19 29 0.817 1.491 24 21 1.226 1.445 Symptoms and associated behaviors 5 16 1.605 1.280 6 52 -0.061 1.293 12 12 1.582 1.751 16 24 1.075 1.440 17 17 1.648 1.184 20 40 0.387 1.350 Assessment and diagnosis 7 67 -0.492 2.415 9 44 0.199 2.561 13 36 0.447 2.439 14 24 0.783 2.759 15 23 0.809 2.945 18 51 -0.011 2.224 21 26 0.738 2.469 22 27 0.768 2.068 23 19 1.079 1.981 25 29 0.634 2.624 Treatment 8 26 1.313 0.914 10 17 2.113 0.858 11 29 1.091 0.963 The mean value of the ASKSP-R scale (revised Chinese version) was 1.775, ranging from 0.858–2.945. Very little differentiation indicated that the items were insufficient for estimating the subjects' abilities, and too much differentiation affected the results and generated bias. In conjunction with our results, the degree of discrimination should be between 0.30 and 3 ( 31 ). All the items had a discrimination scale > 0.5 and < 3, indicating that all the items were valid. There were three medium discrimination items, items 8, 10, and 11, all in the "treatment" dimension; the lowest discriminating item was item 10 (0.858). Moreover, highly discriminatory items were 1, 2, 3, 4, 5, 6, 7, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, and 25, with the highest discriminatory item being item 15 (2.945, Table 6 and Fig. 3 ). The item characteristic curve (ICC) was plotted on the basis of the differentiation of questions via the 2PL model. The ICC, represented by a logistic curve, showed that the probability of subjects answering questions correctly increased with their knowledge level. As depicted in Fig. 3 , the logistic curve slope for question 15 was the steepest, indicating that it had the highest discriminatory power. Conversely, the slope for question 19 was the shallowest, suggesting that this question had the least discrimination. The 2PL model also facilitated the plotting of estimated total score expectations on the basis of the difficulty of the items, as illustrated by the logistic curve in Fig. 4 . The total points earned by the subjects for correctly answering the questions increased with their knowledge level. 3.4 Distinguishing validity 3.4.1 Discriminant validity. As shown in Table 7 , significant correlations ( p < 0.01) were observed between etiology and epidemiology, treatment, symptoms, and associated behaviors as well as assessment and diagnosis. The absolute values of the correlation coefficients were < 0.5, and all were less than the square root of the corresponding AVEs. This suggested significant discriminant validity between the latent variables. Moreover, each latent variable could effectively discriminate between the different knowledge dimensions. Table 7 Distinguishing validity of all dimensions Dimension Etiology and epidemiology Curing Symptoms and associated behaviors Assessment and diagnosis Etiology and epidemiology 0.39 Curing 0.031*** 0.394 Symptoms and associated behaviors 0.027*** 0.023*** 0.387 Assessment and diagnosis 0.044*** 0.039*** 0.033*** 0.401 AVE square root 0.6245 0.6277 0.6221 0.6332 *** represents a p value < 0.01, and the diagonal line represents the AVE evaluation variance extraction. 3.4.2 Correctness and standard error for each dimension. We calculated each dimension’s correctness percentage and corresponding standard error to assess the performance of the subjects in different dimensions. The overall accuracy rate was 28.86%, and the dimensions were treatment (23.81%), etiology and epidemiology (24.03%), symptoms and associated behaviors (26.73%), and assessment and diagnosis (34.55%, Table 8 ). Table 8 Accuracy and standard errors of all the dimensions Dimension Correctness rate(%) Standard deviation Etiology and epidemiology 24.03 0.427 Symptoms and associated behaviors 26.73 0.443 Assessment and diagnosis 34.55 0.476 Curing 23.81 0.426 Umbrella 28.86 0.453 3.5 Scale validity To verify the validity of the ASKSP-R scale, we used the ASK-Q questionnaire (Chinese version) to validate the ASKSP-R scale (Chinese version). We tested the reliability and validity of the ASK-Q questionnaire with 49 items. With a KMO = 0.921, the results indicated that the sampling aptitude was good. The Cronbach's α of 0.838 denoted the scale’s high internal consistency. On the basis of these findings, the Chinese version of the ASK-Q can be used as a validity scale to validate the ASKSP-R scale. Our results showed that the ASK-Q scores were significantly correlated with the ASKSP-R scores ( p < 0.001), indicating good validity. The regression analysis results revealed that the standardized coefficient of the ASKSP-R total score to the ASK-Q total score was 0.421. This indicated a positive correlation between them. The R2 value of 0.177 indicated that the model explained the raw data to a high degree, thereby consolidating the reliability of the ASKSP-R scale (Chinese version). The regression model results are shown in Table 9 , and Table 10 displays the specific results of the model parameters. Table 9 Regression model analysis results Unstandardized coefficient Standardized coefficient t Significance B standard error β (Constant) 31.543 0.141 223.531 0.000 ASKSP-R total score 0.362 0.015 0.421 23.411 < 0.001 Table 10 Model parameters R R 2 Adjusted R 2 Errors in standard estimates F Significance 0.421 a 0.177 0.177 4.368 548.08 < 0.000 b a. Predictor variables: (constants), ASKSP-R total score b. Dependent variable: total ASK-Q score 4. Discussion It is important to assess ASD knowledge among doctors, as an understanding of ASD is crucial for diagnosing ASD ( 32 – 35 ). Since a unified scale is necessary to measure Chinese professionals' ASD knowledge, we conducted a localized revision of the ASKSP-R scale (Chinese version). Our study revealed that the revised Chinese version of the ASKSP-R had good reliability and validity, especially structural validity and discriminant validity, with significant advantages. This validated the scale’s applicability in the Chinese cultural and linguistic environment and could be used to assess the knowledge of Chinese ASD professionals accurately. Compared with other ASD knowledge scales, the multidimensional structure of the ASKSP-R is more clinically relevant and precisely captures the cognitive differences among professionals in different knowledge domains. The scale is professional, has moderate entries, and is feasible. 4.1 Scale revision methodology Expert consultation is necessary for scale development and revision. The scale’s four dimensions were finalized through expert consultation and literature references, namely, etiology and epidemiology, treatment, symptoms, and associated behaviors, as well as assessment and diagnosis [46] . The expert consultation scale is based mainly on the scale’s accuracy, retention status, applicability to China, and dimensions. The ASKSP-R Scale (Revised Chinese Version) was revised after discussions with core group members and experts as well as a literature search. The ASKSP-R Scale (Revised Chinese Version) was revised to respect actual clinical practice. The final version has 25 items. 4.2 Scale reliability Our results revealed that the scale’s internal consistency reliability coefficient was 0.885 and exceeded the threshold criterion of 0.8. This indicated that the scale had good internal consistency and was suitable as an ASD knowledge measurement tool for Chinese professionals. Moreover, all four dimensions' CRs were > 0.7, thereby validating the scale’s internal consistency. 4.3 Scale validity In terms of structural validity, the scale’s KMO value was 0.888, and Bartlett's test of sphericity also passed the test of significance ( p < 0.01), indicating the suitability of the ASKSP-R scale (Chinese version) for factor analysis. Additionally, the fragmentation plot and the validated factor analysis results confirmed the scale’s multidimensional structure. These findings indicate that the ASKSP-R scale (Chinese version) has good validity. We used the expert consultation method and a rubble diagram (Fig. 1 ) to analyze the scale’s factors and the dimensions of various entries. Four common factors were extracted from the exploratory factor analysis to form four dimensions, namely, etiology and epidemiology, symptoms and related behaviors, assessment and diagnosis, and treatment. The scale’s multidimensionality was confirmed by validated factor analysis. The X 2 /df (chi-square degrees of freedom ratio) was 9.862, which might be due to a more complex model as well as a larger sample size. However, this large value was within acceptable limits. The AVE, AVE square root, and CR values of each dimension were acceptable. However, the CR was > 0.7, indicating that the ASKSP-R scale (Chinese version) had an acceptable convergence effect and superior discriminant validity. Thus, the Chinese Revised version of the ASKSP-R might be a reliable and valid method for measuring ASD knowledge in the Chinese population. Hence, the overall model’s fit for etiology and epidemiology, treatment, symptoms, and related behaviors, as well as assessment and diagnosis, was good, with an acceptable convergent effect. This finding indicates that all four dimensions were correlated and distinguishable from each other, thereby denoting an ideal discriminant validity of the scale data. The ASKSP-R scale (Chinese version) was significantly and positively associated with the validity scale, indicating that the trend of the subjects' ASKSP-R scale (Chinese version) was consistent with that of the ASK-Q questionnaire (Chinese version). This indicated the scale’s validity and reliability. 4.4 Item response theory The "treatment" dimension’s CR was 0.66, and the discrimination scores were all 1), which made it difficult to differentiate between the subjects' knowledge levels. We hypothesize that the greater difficulty with questions 10 and 11 may be due to poor knowledge about equestrian therapy in China. Xiao et al. (2023) explored the effects of equestrian-assisted activities and therapies for individuals with ASD in a systematic review. Although equine therapy can significantly improve social and behavioral functioning in ASD children, the effects are inconsistent across various subdomains (e.g., social awareness, motivation, and stereotyped behaviors). Thus, the effectiveness of therapy in different cultural contexts should be further investigated. Although "equine therapy" is widely recognized as an emerging intervention for ASD in theory, its practical application and awareness are still low ( 36 ). In China, very few relevant studies exist, leading to a lack of ASD knowledge among relevant professionals. This was also confirmed in our subsequent expert consultation. Therefore, this may explain the high difficulty levels of the questions and the "treatment" dimension’s low degree of differentiation. Nonetheless, the scale’s overall differentiation was good, and the difficulty level was within the acceptable range. These findings suggested that the overall validity of the ASKSP-R (Chinese version) scale was good. Although the discrimination level of the "treatment" dimension was low, the overall discrimination level of the scale was satisfactory, with acceptable difficulty levels. This suggests that the scale's overall validity is robust. The study intentionally avoided autism as a condition to be ‘treated’ in a traditional medical sense. Instead, the concentration was on interventions and support strategies designed to enhance quality of life, facilitate skill development, and address cooccurring conditions that could impact functioning. 4.5 Research innovations and shortcomings The primary innovation of our study lies in the successful adaptation of the ASKSP-R scale for the Chinese population, creating a knowledge assessment tool for Chinese ASD professionals through rigorous localization and reliability testing. This effort could address the research gap in this area and provide a solid scientific basis for future clinical applications and training assessments. The scale’s use can enhance ASD diagnosis and intervention measures in China, improve the knowledge of relevant professionals, and ultimately offer more effective support for individuals with ASD. Consequently, we demonstrated the applicability of the Chinese version of the ASKSP-R scale for assessing the knowledge of Chinese ASD professionals by validating its reliability and validity. However, several limitations in this study need to be addressed. First, despite efforts to collect a representative sample, geographical and gender diversity was limited because of the use of random and snowball sampling from selected hospitals. This might impact the scale’s generalizability to a broader population. Therefore, future studies should aim for a larger, multicenter sample to better validate the scale’s applicability. Second, the impact of different cultural contexts on ASD knowledge perceptions was not considered. Future research will examine the adaptability of the ASKSP-R scale in various cultural settings through cross-cultural studies. Additionally, the second sample included only 26.65% men, which led to an underrepresentation of this group. As the ASK-Q (Chinese version) is an ASD knowledge scale designed for the general population and has not been validated for professionals, differences in the knowledge dimensions and focus between the general population and professionals might account for some of the observed differences in results. Given the diverse professional roles of participants, there might be variability in the level of ASD knowledge and the specific challenges faced by different groups, such as medical professionals and special education teachers. While this diversity reflects the multidisciplinary nature of ASD management, future studies will explore the specific needs and knowledge gaps within each subgroup. This study included a diverse group of professionals involved in ASD management, ranging from medical practitioners to special education teachers. While this approach provides valuable insights into the multidisciplinary nature of ASD care, it also introduces potential biases due to differences in participants’ roles and experiences. For example, professionals in specialist schools might primarily serve children with severe ASD, which could affect their perspectives and knowledge compared with those working in mainstream settings. Additionally, while the presurvey helped refine the scale, its small sample size might limit the generalizability of feedback to the broader participant population. Future studies should consider stratified sampling and larger presurvey groups to address these limitations. 5. Conclusions In summary, the Chinese version of the ASKSP-R was revised as the first ASD knowledge scale for professionals in China. With strong reliability and validity, it serves as an evaluation tool to assess the knowledge of Chinese professionals regarding autism-related disorders. This study contributes to enhancing the knowledge and expertise of those working with ASD while also supporting the earlier identification of ASD patients by medical practitioners. This can significantly help ASD patients receive tailored rehabilitation programs, such as social skills training, speech therapy, and behavioral interventions, and customize these interventions early. Declarations Ethics approval and consent to participate All methods in the study were carried out in accordance with relevant guidelines and regulations or in accordance with the Declaration of Helsinki. The studies involving human participants were reviewed and approved by Guizhou Nursing Vocational College Ethics Review Committee. All participants signed an informed consent form before participation and had the option of withdrawing at any time. Consent for publication The manuscript does not include any detailed information, images or video material relating to individuals, so consent for publication is not applicable for the study. Availability of data and material The raw data supporting the conclusions of this article will be made available by the authors on request. Funding This study was supported by the Guizhou nursing vocational college (No. Gzhly2023-03) Competing interests The authors have no competing interests to declare that are relevant to the content of this article. Authors' contributions Conceptualization, Yueying Zhang; Feng Hong; Zhujun Zhao and Yang Hong; methodology,Yueying Zhang; Fang Hou; software, Yueying Zhang; validation, Yueying Zhang, Yang Hong; formal analysis, Yueying Zhang; investigation, Yueying Zhang; Fudong Li; resources, Yueying Zhang; data curation, Yueying Zhang; writing—original draft preparation, Yueying Zhang; writing—review and editing, Yueying Zhang, Yang Hong; visualization, Yueying Zhang; supervision, Yang Hong; project administration, Yueying Zhang. All authors have read and agreed to the published version of the manuscript. Acknowledgements We would like to thank the author of the original scale, Dr. McClain, for her kind help and all the research participants for their participation. Clinical trial number: Not applicable References Kanner L. Irrelevant and metaphorical language in early infantile autism. Am J Psychiatry. 1946;103(2):242-6. American Psychiatric Association D, American Psychiatric Association D. Diagnostic and statistical manual of mental disorders: DSM-5: American psychiatric association Washington, DC; 2013. Zeidan J, Fombonne E, Scorah J, Ibrahim A, Durkin MS, Saxena S, et al. Global prevalence of autism: A systematic review update. Autism Res. 2022;15(5):778-90. Proposal for the China Disabled Persons' Federation to treat autism as a separate type of disability: China Disabled Persons' Federation; 2020 [Available from: https://www.cdpf.org.cn//ztzl/zyzt1/qglhjytafw/2020nqglhjytablfwgk1/36208c941759417084733b33112602ef.htm. Baio J, Wiggins L, Christensen DL, Maenner MJ, Daniels J, Warren Z, et al. Prevalence of Autism Spectrum Disorder Among Children Aged 8 Years - Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2014. MMWR Surveill Summ. 2018;67(6):1-23. Christensen DL, Baio J, Van Naarden Braun K, Bilder D, Charles J, Constantino JN, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among Children Aged 8 Years--Autism and Developmental Disabilities Monitoring Network, 11 Sites, United States, 2012. MMWR Surveill Summ. 2016;65(3):1-23. Rogers SJ, Estes A, Lord C, Munson J, Rocha M, Winter J, et al. A Multisite Randomized Controlled Two-Phase Trial of the Early Start Denver Model Compared to Treatment as Usual. J Am Acad Child Adolesc Psychiatry. 2019;58(9):853-65. Christensen DL, Bilder DA, Zahorodny W, Pettygrove S, Durkin MS, Fitzgerald RT, et al. Prevalence and Characteristics of Autism Spectrum Disorder Among 4-Year-Old Children in the Autism and Developmental Disabilities Monitoring Network. J Dev Behav Pediatr. 2016;37(1):1-8. McNally Keehn R, Ciccarelli M, Szczepaniak D, Tomlin A, Lock T, Swigonski N. A Statewide Tiered System for Screening and Diagnosis of Autism Spectrum Disorder. Pediatrics. 2020;146(2). Courchesne E, Pramparo T, Gazestani VH, Lombardo MV, Pierce K, Lewis NE. The ASD Living Biology: from cell proliferation to clinical phenotype. Molecular Psychiatry. 2018;24(1):88-107. Schreibman L, Dawson G, Stahmer AC, Landa R, Rogers SJ, McGee GG, et al. Naturalistic Developmental Behavioral Interventions: Empirically Validated Treatments for Autism Spectrum Disorder. J Autism Dev Disord. 2015;45(8):2411-28. Zwaigenbaum L, Bauman ML, Choueiri R, Kasari C, Carter A, Granpeesheh D, et al. Early Intervention for Children With Autism Spectrum Disorder Under 3 Years of Age: Recommendations for Practice and Research. Pediatrics. 2015;136 Suppl 1(Suppl 1):S60-81. Harstad E, Hanson E, Brewster SJ, DePillis R, Milliken AL, Aberbach G, et al. Persistence of Autism Spectrum Disorder From Early Childhood Through School Age. JAMA Pediatrics. 2023;177(11). Sun X, Allison C, Matthews FE, Zhang Z, Auyeung B, Baron‐Cohen S, et al. Exploring the Underdiagnosis and Prevalence of Autism Spectrum Conditions in Beijing. Autism Research. 2015;8(3):250-60. Pang Y, Lee CM, Wright M, Shen J, Shen B, Bo J. Challenges of case identification and diagnosis of Autism Spectrum Disorders in China: A critical review of procedures, assessment, and diagnostic criteria. Research in Autism Spectrum Disorders. 2018;53:53-66. Rice CE, Rosanoff M, Dawson G, Durkin MS, Croen LA, Singer A, et al. Evaluating Changes in the Prevalence of the Autism Spectrum Disorders (ASDs). Public Health Rev. 2012;34(2):1-22. Wei H, Li Y, Zhang Y, Luo J, Wang S, Dong Q, et al. Awareness and knowledge of autism spectrum disorder in Western China: Promoting early identification and intervention. Frontiers in Psychiatry. 2022;13. Unigwe S, Buckley C, Crane L, Kenny L, Remington A, Pellicano E. GPs’ confidence in caring for their patients on the autism spectrum: an online self-report study. British Journal of General Practice. 2017;67(659):e445-e52. Su L, Lin Z, Li Y, Wei L. Autism spectrum disorder knowledge scale: Chinese revision of the general population version. BMC Psychiatry. 2023;23(1):66. Lu M, Wang R, Zou Y, Pang F. Chinese College Students' Knowledge of Autism Spectrum Disorder (ASD) and Social Distance from Individuals with ASD: The Mediating Role of Negative Stereotypes. J Autism Dev Disord. 2022;52(8):3676-85. Yu L, Stronach S, Harrison AJ. Public knowledge and stigma of autism spectrum disorder: Comparing China with the United States. Autism. 2020;24(6):1531-45. Jenks E, Selman F, Harmens M, Boon S, Tran T, Hobson H, et al. Teaching higher education staff to understand and support autistic students: evaluation of a novel training program. Frontiers in Psychiatry. 2023;14. Gu L, Ye C, He S, Deng C, Chen X, Liao Y, et al. Zhongshan city kindergarten care staff autism. Chinese Journal of School Health. 2016;37(11). Zhang L, Huo J, Gao L, Hao J, Yang W, Yu Y. Analysis of Awareness and Management Attitude towards Autism Spectrum Disorders among Different Populations. Health Medicine Research and Practice. 2019;16(2):23-6. Wang J, Wu J, Yang F, Zhou Y, Sun C, Liang S, et al. Survey of Autism Awareness in Caregivers of Children Aged 3 ~ 6 Years. Chinese Mental Health Journal. 2013;27(6):451-6. Harrison AJ, Naqvi NC, Smit AK, Kumar PN, Muhammad NA, Saade S, et al. Assessing Autism Knowledge Across the Global Landscape Using the ASK-Q. Journal of Autism and Developmental Disorders. 2023;54(5):1897-911. Rafiei M, Nakhostin-Ansari A, Meshkat S, Khosravi A, Memari AH. Public awareness and stigma of autism spectrum disorder in Iran; An online survey. Research in Developmental Disabilities. 2023;134:104441. McClain MB, Harris B, Haverkamp CR, Golson ME, Schwartz SE. The ASKSP Revised (ASKSP-R) as a Measure of ASD Knowledge for Professional Populations. J Autism Dev Disord. 2020;50(3):998-1006. Harrison AJ, Bradshaw LP, Naqvi NC, Paff ML, Campbell JM. Development and Psychometric Evaluation of the Autism Stigma and Knowledge Questionnaire (ASK-Q). J Autism Dev Disord. 2017;47(10):3281-95. Nohra J, Sacre Y, Abdel-Nour A, Mannan H, Khajuria D. Evaluation of Knowledge, Attitudes, and Practices Related to Osteoporosis and Correlates of Perceived High Risk among People Living in Two Main Districts of Lebanon. Journal of Osteoporosis. 2022;2022:1-8. Liu B, Wen Z, Zhou L. Revision of the perioperative recovery scale for integrative medicine based on item response theory. Chinese Journal of Evidence-Based Medicine. 2024;24(4):439-44. Gore K, Gilbert M, Hawke M, Barbaro J. Investigating autism knowledge, self-efficacy, and confidence following maternal and child health nurse training for the early identification of autism. Frontiers in Neurology. 2024;14. Waddington H, Shepherd D, van der Meer L, Powell-Hector N, Wilson E, Barbaro J. Brief Report: Training New Zealand Well Child/Tamariki Ora Nurses on Early Autism Signs Using the Social Attention and Communication Surveillance-Revised. Journal of Autism and Developmental Disorders. 2021;52(11):5050-7. Clarke L, Fung LK. The impact of autism-related training programs on physician knowledge, self-efficacy, and practice behavior: A systematic review. Autism. 2022;26(7):1626-40. Ben-Sasson A, Atun-Einy O, Yahav-Jonas G, Lev-On S, Gev T. Training Physical Therapists in Early ASD Screening. Journal of Autism and Developmental Disorders. 2018;48(11):3926-38. Borgi M, Loliva D, Cerino S, Chiarotti F, Venerosi A, Bramini M, et al. Effectiveness of a Standardized Equine-Assisted Therapy Program for Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders. 2015;46(1):1-9. Additional Declarations No competing interests reported. Supplementary Files ChineseversionofASKSPRENrevised.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-6184251","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":438768541,"identity":"66b623ea-ab32-4ca8-a9bc-8c526ff68905","order_by":0,"name":"Yueying Zhang","email":"","orcid":"","institution":"Guizhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yueying","middleName":"","lastName":"Zhang","suffix":""},{"id":438768542,"identity":"dc9f8312-bc4b-44fc-8403-aaf50a72e059","order_by":1,"name":"Zhujun Zhao","email":"","orcid":"","institution":"Guizhou nursing vocational 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13:08:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6184251/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6184251/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80582073,"identity":"bf014048-b43b-45e1-9f17-a80265b980ac","added_by":"auto","created_at":"2025-04-14 23:25:19","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":23871,"visible":true,"origin":"","legend":"\u003cp\u003eGravel diagram denoting the scale’s multidimensional structure\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6184251/v1/f9768e50cdc823da68261b5e.jpg"},{"id":80582074,"identity":"dfd5334f-2c64-4241-8fca-5a79ccc7f1b5","added_by":"auto","created_at":"2025-04-14 23:25:19","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65075,"visible":true,"origin":"","legend":"\u003cp\u003eValidated factor analysis (CFA) showing the multifactor structural model\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6184251/v1/86b6afaca5ae48e61d8a9754.jpg"},{"id":80582541,"identity":"21cdf802-35e2-411d-b986-91c1e69f00d7","added_by":"auto","created_at":"2025-04-14 23:33:19","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":71569,"visible":true,"origin":"","legend":"\u003cp\u003eItem Characterization Curve (ICC)\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6184251/v1/ac05a32246e0c9ea8bc98f38.jpg"},{"id":80582076,"identity":"bceeef67-c132-450e-aac2-4299c4a461e0","added_by":"auto","created_at":"2025-04-14 23:25:19","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":19727,"visible":true,"origin":"","legend":"\u003cp\u003eExpected values of the estimated total scores\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6184251/v1/e3fb30470b1b27717f2ca7ef.jpg"},{"id":88900576,"identity":"eb783b97-bc07-4629-81c2-91611c41047e","added_by":"auto","created_at":"2025-08-12 13:38:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1519065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6184251/v1/04d270c5-b163-44cd-9a3f-a8d12b214fa7.pdf"},{"id":80582850,"identity":"8588869b-072f-42a2-9a2a-d0a408c7bde1","added_by":"auto","created_at":"2025-04-14 23:41:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":19735,"visible":true,"origin":"","legend":"","description":"","filename":"ChineseversionofASKSPRENrevised.docx","url":"https://assets-eu.researchsquare.com/files/rs-6184251/v1/ed36674b1b2666f862424896.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Localization of the Autism Spectrum Disorder Knowledge Scale Professional Version (ASKSP-R) in Western Cities of China: A Case Study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAutism spectrum disorder (ASD) was first proposed by Kanner (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5\u003csup\u003e\u0026trade;\u003c/sup\u003e), specifies persistent deficits in social communication and interaction, as well as restricted and repetitive behaviors as the main characteristics of ASD (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Recently, the global prevalence of ASD has increased significantly, and approximately 1% of children are diagnosed with ASD (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Over 10\u0026nbsp;million people in China have been diagnosed with ASD, including approximately 2\u0026nbsp;million children aged younger than 12 years. The number of new ASD cases is increasing at a rate of \u0026gt;\u0026thinsp;100,000 additional children per year (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Despite the increasing prevalence of ASD, the issue of delayed diagnosis persists. While the average age at diagnosis is 4\u0026ndash;5 years, the majority of ASD children do not undergo developmental assessment before the age of 3 years (\u003cspan additionalcitationids=\"CR6 CR7 CR8\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Delayed diagnosis prevents young children from benefiting from their optimal neuroplasticity period. In addition to missing out on the ideal time to intervene, it also increases the burden on both individuals and society (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). Hence, early diagnosis and interventions have significantly improved the prognosis of ASD children and reduced long-term social expenditure (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Moreover, a lack of expertise in relevant professionals can cause misdiagnosis or delayed diagnosis and slow the patient\u0026rsquo;s recovery process (\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, ASD knowledge in the general Chinese population is low. Although the level of ASD knowledge among medical professionals is particularly important, there is currently no assessment tool for understanding the level of ASD knowledge among Chinese professionals (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Thus, a comprehensive assessment tool is needed to understand and improve ASD knowledge among professionals. In the Chinese context, ASD diagnosis and intervention involve a multidisciplinary approach. Screening mainly begins with pediatricians during routine health checkups, followed by referrals to psychologists or neurologists for further evaluation. Educational and rehabilitation professionals, such as special education teachers, are essential in implementing interventions tailored to the child's needs. Although formal prequalification training in ASD is not universally required for all professionals, psychologists and medical practitioners typically receive some training as part of their professional education. However, the depth of this training varies widely, and additional professional development is often needed to ensure adequate knowledge and skills for ASD-related tasks.\u003c/p\u003e \u003cp\u003eIn recent years, domestic studies on ASD knowledge have focused mainly on the general population. Few studies have targeted ASD professionals, and most existing questionnaires are self-developed questionnaires (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e) and lack a scientific basis. A few translated foreign scales are available for the general population (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Very few ASD knowledge scales have been developed for relevant professionals. The general Chinese population has limited awareness of ASD (Yu et al., 2020), with the majority unaware that effective interventions and support strategies are available for individuals with ASD (Su et al., 2023). Furthermore, many people are unaware of where skill-building interventions or specialized training programs for ASD are most effectively implemented (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). A study by Huang et al. (2012) indicated that approximately 80% of parents choose \u0026ldquo;wait and see\u0026rdquo; if their child develops abnormal behaviors as they grow older. Sullivan et al. (2023) suggested that the lowest level of ASD knowledge in the general population was in the area of ASD etiology. However, Lu et al. (2022) revealed that the general population is aware that children should be taken to the hospital after they develop abnormalities. A majority of them also consider that ASD requires long-term skill-building interventions or specialized training programs and that symptoms can be improved by long-term training. Presently, the questionnaire used to measure ASD knowledge in the general Chinese population is the Autism Stigma and Knowledge Questionnaire (ASK-Q), which was modified by Lodi et al. This questionnaire has been widely used domestically and internationally (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). The majority of the general population is unaware of the primary symptoms of ASDs, ASD cooccurring with intellectual disability, and prognosis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Gu et al.\u0026rsquo;s questionnaire, exclusively for kindergarten healthcare providers, did not include a scale for ASD professionals (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). Although China has various ASD knowledge questionnaires, most of these questionnaires are aimed at the general population or caregivers, and there is a lack of ASD scales specifically for professionals (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eResearch on ASD in foreign countries remains in its early stages, with a particular concentration on the knowledge of ASD professionals. Several international questionnaires have been developed to assess ASD knowledge, including the Knowledge about Childhood Autism among Health Workers (KCAHW) scale by Bakare et al. (2008), the Autism Knowledge Scale by Unigwe et al. (2017), the Autism Knowledge Scale by Crane et al. (2019), and the Autism Spectrum Knowledge Scale Professional Version-Revised (ASKSP-R) by McClain et al. (2020). The Autism Knowledge Scale by Unigwe et al. (2017) is a relatively new instrument based on an earlier scale by Zwaigenbaum et al. (2015). However, it provides only \u0026ldquo;correct\u0026rdquo; and \u0026ldquo;incorrect\u0026rdquo; response options, omitting a \u0026ldquo;don\u0026rsquo;t know\u0026rdquo; choice, which limits its ability to fully capture a doctor\u0026rsquo;s actual ASD knowledge. While the scale by Crane et al. (2019) is more comprehensive than that of Unigwe et al. (2017), it lacks a rehabilitation component, is more cumbersome to use, and demands greater effort. The KCAHW questionnaire, developed by Bakare et al. (2008), is widely used and frequently referenced. However, it lacks coverage of diagnostic aspects and does not provide sufficient detail for specialized medical professionals. In contrast, the ASKSP-R scale is praised for its scientific rigor and comprehensiveness, although no Chinese version is currently available.\u003c/p\u003e \u003cp\u003eThe ASKSP-R scale for ASD professionals, developed by McClain et al. (2024), provides comprehensive knowledge coverage, demonstrates strong reliability and validity, and aligns with the DSM-5 by addressing multiple disease content areas. To reduce guesswork, the scale includes a \u0026ldquo;do not know\u0026rdquo; response option alongside the traditional \u0026ldquo;correct\u0026rdquo; and \u0026ldquo;incorrect\u0026rdquo; options (McMahon et al., 2024). However, a Chinese version of the scale has not yet been developed (McClain et al., 2020). Consequently, the scale has been translated for this purpose. While there is no cure for autism, evidence-based interventions, such as behavioral therapies, educational support, and targeted skill-building strategies, can significantly improve the outcomes and quality of life of individuals with ASD.\u003c/p\u003e \u003cp\u003eThis study aimed to translate and adapt the ASKSP-R scale to assess ASD professional knowledge in China through localized revisions. It also aimed to evaluate the current knowledge levels of Chinese ASD professionals, identify factors influencing their understanding of ASD, and develop targeted training programs to increase their expertise. These efforts are expected to improve early diagnosis and intervention for ASD, thereby enhancing affected patients\u0026rsquo; quality of life.\u003c/p\u003e "},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Scale revision\u003c/h2\u003e \u003cp\u003eAfter translation, the scale was revised via the TRAPD (Translation, Review, Adjudication, Pretesting, and Documentation) model, with consent from the original authors (Chow et al., 2021). First, two professional translation teams independently translated and reviewed the scale. A third qualified translator then reviewed the translation, conducted an accuracy check, and completed the reviewer\u0026rsquo;s prescreening. Additionally, the scale was modified through expert consultations and a literature review. Two senior ASD experts, with extensive experience in both research and clinical practice, were consulted to refine the questionnaire content. Their input was crucial for adapting the scale to the Chinese context and ensuring its localization. A presurvey was conducted to assess the scale\u0026rsquo;s feasibility and comprehensibility in real-world use. On the basis of the presurvey feedback, several questions were revised to increase the scale\u0026rsquo;s clarity and applicability. The revised version was again reviewed and confirmed by the ASD experts, resulting in the final Chinese version of the ASKSP-R scale. On the basis of the experts\u0026rsquo; comments and relevant policy literature, modifications were made to the ASKSP-R scale, which are listed in the Supplementary File \u0026lsquo;Chinese version of ASKSP-R(EN-revised)\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study subjects\u003c/h2\u003e \u003cp\u003eThe study participants included physicians (e.g., pediatricians, neurologists) responsible for ASD screening and diagnosis and special education teachers involved in intervention and rehabilitation. The study aimed to include a mix of professionals from both specialist and general settings, such as hospitals and special education schools, to capture a wide range of expertise. However, professionals in specialist schools may work predominantly with children with more severe needs, which could influence their perspectives and knowledge. Future research could consider stratified sampling to better represent different professional subgroups and settings. Professionals involved in ASD diagnosis and care in China include psychologists, who provide assessments and initial diagnoses; medical practitioners (e.g., pediatricians, neurologists, child health doctors), who are responsible for confirming diagnoses and developing intervention plans tailored to the individual\u0026rsquo;s needs and cooccurring conditions; and special education teachers, who contribute to intervention and rehabilitation programs.\u003c/p\u003e \u003cp\u003eThe scale was based on random and snowball sampling methods. A total of 2,550 questionnaires were distributed, and 2548 valid questionnaires were returned, for a recovery rate of 99.92%. Our study population comprised teachers\u0026thinsp;\u0026ge;\u0026thinsp;18 years old, those working in special education schools, and physicians directly involved in ASD screening, diagnosis, and intervention. Those with incomplete questionnaires or who did not provide informed consent were excluded to ensure the accuracy and reliability of the data. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the participants\u0026rsquo; demographic information, including age, sex, education, title, ethnicity, whether they have interacted with ASD children, whether they know about ASD, and how they learned about it. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the patients\u0026rsquo; demographic information.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatients\u0026rsquo;demographic data\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConsiderations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of participants\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;2548(%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(a person\u0026rsquo;s) age\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e202(7.93)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u0026ndash;35 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1462(57.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36\u0026ndash;45 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e664(26.06)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46\u0026ndash;55 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e192(7.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e25(0.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60 years and over\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3(0.12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003edistinguishing between the sexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003emale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e679(26.65)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1869(73.35)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eacademic qualifications\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTechnical secondary school and below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6(0.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege/Undergraduate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2434(95.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePostgraduate and above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e108(4.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etitle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e302(11.85)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1164(45.68)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiddle title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e810(31.79)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVice-senior title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e206(8.08)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSenior title\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66(2.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003enation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHan ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1491(58.52)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMiao ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e313(12.28)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTujia ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e136(5.33)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDong ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147(5.76)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBuyi ethnic group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e174(6.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethe rest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e287(11.24)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eExposure to children with ASD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1190(46.70)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1358(53.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eWhether you have learnt about ASD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1015(39.84)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1533(60.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIn what way do you learn about ASD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-study\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e625(24.53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eI learnt it in school.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e464(18.20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnified on-site training from company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e379(14.86)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnified online training from company\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e322(12.62)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ethe rest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e152(5.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection and procedures\u003c/h2\u003e \u003cp\u003eWe collected data from April 2024 to May 2024. The specific steps were as follows: presurvey period: A presurvey was conducted from 13 to 18 February 2024 with 32 participants, including a mix of medical professionals and special education teachers, to evaluate the feasibility, clarity, and cultural relevance of the translated scale. Participant feedback from the presurvey highlighted areas where question phrasing needed to be simplified or localized. These changes were essential to ensure that the scale was understandable and relevant to Chinese professionals. The finalized scale was further tested for consistency and clarity before full-scale data collection. Feedback from this phase identified ambiguities in certain questions and informed adjustments to improve clarity and applicability. For example, terminology inconsistencies and region-specific content were refined to better align with Chinese professionals\u0026rsquo; practices. These modifications were reviewed and approved by two senior ASD experts.\u003c/p\u003e \u003cp\u003ePreparation period: We contacted hospital directors to ensure precise questionnaire distribution and data collection. In terms of the questionnaire distribution, the Chinese version of the ASKSP-R scale was distributed to ASD-related departments such as pediatrics, rehabilitation, neurology, psychiatry, and schools for special children after the consent of the head of the hospital was obtained. The head of each department and school was responsible for administering the questionnaire and explaining it to the participants.\u003c/p\u003e \u003cp\u003eBefore starting the survey, the questionnaire mentioned the study\u0026rsquo;s purpose, estimated completion time, and content of the informed consent form. The participants completed the questionnaire voluntarily to ensure their right to information and willingness to participate.\u003c/p\u003e \u003cp\u003eData were collected via random sampling and snowball sampling. First, several hospitals were randomly selected for our survey. The questionnaire\u0026rsquo;s QR code was subsequently sent to the interviewed doctors and special education teachers through the Questionnaire Star platform. This was further shared with other professionals for a snowball effect and to expand the sample coverage. Considering the extensive geographical distribution of the participating hospitals and the workload of the target population, we adopted an electronic questionnaire format and unified data collection through the Questionnaire Star platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.wjx.cn/login.aspx\u003c/span\u003e\u003cspan address=\"https://www.wjx.cn/login.aspx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The Questionnaire Star platform is a free online questionnaire survey and evaluation platform that includes an efficient and convenient online questionnaire design and data collection method and is widely used in several Chinese studies. With this platform, researchers can design questionnaires online and independently, with the integration of questionnaire quantity, time, and location. Because it is more efficient and convenient, it is widely used in China. Before beginning the survey, the heads of the participating hospitals and schools were responsible for facilitating the distribution of the questionnaires and providing general information to the participants. However, the consent process was strictly individual. The participants were provided with detailed information about the study\u0026rsquo;s purpose, the estimated time commitment, and the content of the informed consent form. They were then asked to provide informed consent voluntarily before completing the questionnaire. This ensured that the administration by the heads of facilities did not influence the autonomy of participants\u0026rsquo; decisions to participate. All participants answered a few social and demographic questions and completed the ASKSP-R scale and the ASK-Q questionnaire.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Ethics\u003c/h2\u003e \u003cp\u003e Our study was approved by the Ethics Committee of Guizhou Nursing Vocational College. Our approval number was gzhlllscb2024-0301. All participants signed an informed consent form before participation and had the option of withdrawing at any time. The participants were not harmed physically or psychologically throughout the study. The participants\u0026rsquo; privacy was strictly protected, all personal information (e.g., name, education, etc.) was anonymized, and raw data access was restricted to authorized personnel only. Additionally, our results were presented in a summary without revealing participants\u0026rsquo; personal information and were used for academic research only. The role of the facility heads was strictly logistical, ensuring that participants were informed about the study, but the consent process was conducted independently of each participant. The heads did not influence the participants' decision to consent, which ensured the voluntary and informed nature of their participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Descriptive analysis.\u003c/h2\u003e \u003cp\u003eThe sample\u0026rsquo;s demographic information was analyzed via descriptive statistics via EXCEL. Descriptive statistics included frequencies, percentages, means, and standard deviations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2. Reliability analysis.\u003c/h2\u003e \u003cp\u003eTo assess the questionnaire\u0026rsquo;s internal consistency, we analyzed the reliability of the sample and questionnaire dimensions via SPSS 27.0 and Cronbach's α, respectively. Cronbach's α coefficient is frequently used in reliability analyses of measurement instruments and can be used to assess a questionnaire\u0026rsquo;s internal consistency precisely.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Structural validity.\u003c/h2\u003e \u003cp\u003eFor the structural validity analysis, we first assessed the structural validity of the ASKSP-R scale (Chinese version) via confirmatory factor analysis (CFA). AMOS26 software was subsequently used to conduct validated factor analysis on the ASKSP-R scale (Chinese version). The analysis metrics included parameters such as the chi-square test (χ2), degrees of freedom (df), comparative fit index (CFI), Tucker Lewis Index (TLI), and root mean square error of approximation (RMSEA). The scale\u0026rsquo;s validity was subsequently consolidated by calculating convergent (CR values) and discriminant (AVE and its square root) validities.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Item response theory.\u003c/h2\u003e \u003cp\u003eItem response theory (IRT) is a statistical method for analyzing test questions and subjects' abilities. There are three commonly used models: the one-parameter logistic model (Rasch model), the two-parameter logistic model (2PL model), and the three-parameter logistic model (3PL model). In this study, the 2PL model was used to analyze the scale items according to the scale dimensions and the model complexity. The differentiation (a) and difficulty (b) parameters of each item were estimated to select the appropriate items. The 2PL model-specific Eq.\u0026nbsp;1 was as follows:\u003c/p\u003e\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" width=\"382\" height=\"58\"\u003e\u003c/p\u003e \u003cp\u003ea\u003csub\u003ei\u003c/sub\u003e is the differentiation parameter of the ith entry.\u003c/p\u003e \u003cp\u003eb\u003csub\u003ei\u003c/sub\u003e is the difficulty parameter of the ith entry.\u003c/p\u003e \u003cp\u003eIRT was conducted through R Studio (version 4.2.2). In the 2PL IRT model, the scale was analyzed primarily by difficulty (b) and differentiation (a) parameters, where (b) reflects the difficulty level of the questions and (a) reflects the ability of the question to distinguish between subjects with varying knowledge. A difficulty parameter of \u0026lt;-1 was considered easy; -1 to +\u0026thinsp;1 was considered moderately difficult, and a parameter\u0026thinsp;\u0026gt;\u0026thinsp;+\u0026thinsp;1 was considered extremely difficult. A differentiation level of \u0026lt;\u0026thinsp;0.5 denoted low differentiation, and the questions were considered less valid; 0.5\u0026ndash;1 suggested medium differentiation, and the questions were considered more valid; and a level\u0026thinsp;\u0026gt;\u0026thinsp;1 denoted high differentiation, with valid questions. The item characteristic curve (ICC) graph represents the association between the subject's ability level (θ) and the probability of answering the question correctly (\u003cem\u003ep\u003c/em\u003e(θ)) as a logistic graph. The estimated total score\u0026rsquo;s expected value plot also represented the correlation between the subject's ability level (θ) and the subject's total score (T(θ)) in the form of a logistic plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.5 Scale validity.\u003c/h2\u003e \u003cp\u003eThe scale\u0026rsquo;s validity was analyzed via the ASK-Q scale compared with the Chinese version of the ASKSP-R scale. The ASK-Q is available in Chinese and contains three dimensions (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e): (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) diagnosis/symptoms, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) etiology, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) treatment, which are similar to the dimensions of the ASKSP-R scale. The internal consistency of the ASK-Q KR-20 coefficient for the Chinese and US samples was 0.72 and 0.82, respectively, within the acceptable range (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Moreover, the ASK-Q is widely used to assess ASD knowledge in the population (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Since the ASK-Q has been used as the validity scale before (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), we chose the ASK-Q as the validity measure of the Chinese ASKSP-R scale.\u003c/p\u003e \u003cp\u003eFor scale validity, we performed linear regression analyses of the ASK-Q and ASKSP-R (Chinese versions) via SPSS 27.0 software.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Calculation of results\u003c/h2\u003e \u003cp\u003eTo measure knowledge, we coded the ASKSP-R scale\u0026rsquo;s knowledge portion. Each participant scored 1 for a correct answer, 0 for an incorrect answer, and 0 for a \"do not know\" answer (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Afterward, the scores were classified as low, medium, or high. Scores between 0 and 9 denote a poor level of knowledge, scores between 10 and 18 indicate medium knowledge, and scores between 18 and 25 suggest a higher level of knowledge (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Reliability test\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the Cronbach's α of the ASKSP-R scale (text version) was 0.885, indicating that the scale\u0026rsquo;s internal consistency was high and that the reliability of the ASKSP-R scale (Chinese version) was satisfactory.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eReliability statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCronbach factor\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem count (of a consignment etc.)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Validity test\u003c/h2\u003e \u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the KMO value was 0.888, indicating that the ASKSP-R (Revised Chinese Version) scale is appropriate for factor analysis. Bartlett's test of sphericity yielded a significance value of 0.000, which is less than 0.01, passing the 1% significance level. This further confirms that the ASKSP-R (Revised Chinese Version) scale is appropriate for factor analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Structural validity\u003c/h2\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Validation factor analysis.\u003c/h2\u003e \u003cp\u003eAccording to the scree plot, the inflection point occurred at 5, suggesting that 4 to 6 factors influence the scale and confirming its multidimensional structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe overall fit coefficients were as follows: (chi-square degrees of freedom ratio) X\u003csup\u003e2\u003c/sup\u003e/df\u0026thinsp;=\u0026thinsp;9.862, RMSEA\u0026thinsp;=\u0026thinsp;0.059 (\u0026lt;\u0026thinsp;0.08), IFI\u0026thinsp;=\u0026thinsp;0.88, CFI\u0026thinsp;=\u0026thinsp;0.88, NFI\u0026thinsp;=\u0026thinsp;0.868, TLI\u0026thinsp;=\u0026thinsp;0.866, and RFI\u0026thinsp;=\u0026thinsp;0.853, which are approximately 0.9 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). These results indicate the model\u0026rsquo;s good fit, acceptable relationships, and complementarities among the factors and the multidimensional structure of the ASKSP-R scale (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eKMO and Bartlett's tests of sphericity values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKMO Quantity of Sample Suitability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eBartlett's test of sphericity\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eapproximate chi-square\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003edegrees of freedom\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003esignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10502.562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOverall model\u0026rsquo;s fit coefficients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX2/df\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRMSEA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTLI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.866\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.853\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe four latent variables, involving etiology and epidemiology, treatment, symptoms and related behaviors, and assessment and diagnosis, demonstrated the highest factor loading (0.716) for item 12 in the \"symptoms and related behaviors\" dimension and the lowest (0.531) for item 6 in the same dimension. The factor loadings for all the items associated with the four latent variables were greater than 0.5, indicating that these latent variables effectively represented the relevant constructs. The average variance extracted (AVE) for each latent variable and the composite reliability (CR) were both greater than 0.36 and 0.66, respectively, suggesting acceptable convergent validity. These findings support the multidimensional structure of the ASKSP-R scale (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFactor loads of all dimensions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003etrails\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEstimate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.792\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.715\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.682\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.570\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.581\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.614\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.716\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.531\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.653\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.622\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.638\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.579\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.607\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;---\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Item response theory.\u003c/h2\u003e \u003cp\u003eIn the 2PL IRT model, the majority of the questions were of medium difficulty. The model had 13 medium-difficulty items, with questions 3, 6, 7, 9, 13, 14, 15, 18, 19, 20, 21, 22, and 25, with question 7 (\"The following professional who can diagnose ASDs is\") being the easiest question and was answered correctly by 67% of the participants (η = -0.492). However, 12 questions were more difficult, with questions 1, 2, 4, 5, 8, 10, 11, 12, 16, 17, 23, and 24. The question with the highest difficulty coefficient was question 10 (\"Which of the following is not an evidence-based intervention for individuals with ASD?\") (η\u0026thinsp;=\u0026thinsp;2.113, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDifficulty and differentiation levels of the ASKSP-R (Chinese version)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle Number\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCorrectness rate(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProblem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDistinctiveness\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.719\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.276\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.302\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.491\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.280\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.293\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.751\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.440\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.648\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.184\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.415\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.561\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.439\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.759\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.809\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.945\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.224\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.469\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.768\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.981\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.624\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.914\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.858\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.963\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe mean value of the ASKSP-R scale (revised Chinese version) was 1.775, ranging from 0.858\u0026ndash;2.945. Very little differentiation indicated that the items were insufficient for estimating the subjects' abilities, and too much differentiation affected the results and generated bias. In conjunction with our results, the degree of discrimination should be between 0.30 and 3 (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). All the items had a discrimination scale\u0026thinsp;\u0026gt;\u0026thinsp;0.5 and \u0026lt;\u0026thinsp;3, indicating that all the items were valid. There were three medium discrimination items, items 8, 10, and 11, all in the \"treatment\" dimension; the lowest discriminating item was item 10 (0.858). Moreover, highly discriminatory items were 1, 2, 3, 4, 5, 6, 7, 9, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, and 25, with the highest discriminatory item being item 15 (2.945, Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe item characteristic curve (ICC) was plotted on the basis of the differentiation of questions via the 2PL model. The ICC, represented by a logistic curve, showed that the probability of subjects answering questions correctly increased with their knowledge level. As depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the logistic curve slope for question 15 was the steepest, indicating that it had the highest discriminatory power. Conversely, the slope for question 19 was the shallowest, suggesting that this question had the least discrimination.\u003c/p\u003e \u003cp\u003eThe 2PL model also facilitated the plotting of estimated total score expectations on the basis of the difficulty of the items, as illustrated by the logistic curve in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The total points earned by the subjects for correctly answering the questions increased with their knowledge level.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Distinguishing validity\u003c/h2\u003e \u003cdiv id=\"Sec21\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Discriminant validity.\u003c/h2\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e, significant correlations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were observed between etiology and epidemiology, treatment, symptoms, and associated behaviors as well as assessment and diagnosis. The absolute values of the correlation coefficients were \u0026lt;\u0026thinsp;0.5, and all were less than the square root of the corresponding AVEs. This suggested significant discriminant validity between the latent variables. Moreover, each latent variable could effectively discriminate between the different knowledge dimensions.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDistinguishing validity of all dimensions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCuring\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.023***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.044***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAVE square root\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.6245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.6277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.6221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.6332\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e*** represents a p value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and the diagonal line represents the AVE evaluation variance extraction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Correctness and standard error for each dimension.\u003c/h2\u003e \u003cp\u003eWe calculated each dimension\u0026rsquo;s correctness percentage and corresponding standard error to assess the performance of the subjects in different dimensions. The overall accuracy rate was 28.86%, and the dimensions were treatment (23.81%), etiology and epidemiology (24.03%), symptoms and associated behaviors (26.73%), and assessment and diagnosis (34.55%, Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAccuracy and standard errors of all the dimensions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDimension\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCorrectness rate(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEtiology and epidemiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e24.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.427\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptoms and associated behaviors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAssessment and diagnosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.476\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCuring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUmbrella\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.453\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Scale validity\u003c/h2\u003e \u003cp\u003eTo verify the validity of the ASKSP-R scale, we used the ASK-Q questionnaire (Chinese version) to validate the ASKSP-R scale (Chinese version). We tested the reliability and validity of the ASK-Q questionnaire with 49 items. With a KMO\u0026thinsp;=\u0026thinsp;0.921, the results indicated that the sampling aptitude was good. The Cronbach's α of 0.838 denoted the scale\u0026rsquo;s high internal consistency. On the basis of these findings, the Chinese version of the ASK-Q can be used as a validity scale to validate the ASKSP-R scale.\u003c/p\u003e \u003cp\u003eOur results showed that the ASK-Q scores were significantly correlated with the ASKSP-R scores (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating good validity. The regression analysis results revealed that the standardized coefficient of the ASKSP-R total score to the ASK-Q total score was 0.421. This indicated a positive correlation between them. The R2 value of 0.177 indicated that the model explained the raw data to a high degree, thereby consolidating the reliability of the ASKSP-R scale (Chinese version). The regression model results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, and Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e displays the specific results of the model parameters.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression model analysis results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnstandardized coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003estandard error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eβ\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e(Constant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.543\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e223.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASKSP-R total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.362\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e23.411\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eModel parameters\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted \u003cem\u003eR\u003c/em\u003e \u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eErrors in standard estimates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eF\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSignificance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.421\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e548.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ea. Predictor variables: (constants), ASKSP-R total score\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eb. Dependent variable: total ASK-Q score\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIt is important to assess ASD knowledge among doctors, as an understanding of ASD is crucial for diagnosing ASD (\u003cspan additionalcitationids=\"CR33 CR34\" citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Since a unified scale is necessary to measure Chinese professionals' ASD knowledge, we conducted a localized revision of the ASKSP-R scale (Chinese version). Our study revealed that the revised Chinese version of the ASKSP-R had good reliability and validity, especially structural validity and discriminant validity, with significant advantages. This validated the scale\u0026rsquo;s applicability in the Chinese cultural and linguistic environment and could be used to assess the knowledge of Chinese ASD professionals accurately. Compared with other ASD knowledge scales, the multidimensional structure of the ASKSP-R is more clinically relevant and precisely captures the cognitive differences among professionals in different knowledge domains. The scale is professional, has moderate entries, and is feasible.\u003c/p\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Scale revision methodology\u003c/h2\u003e \u003cp\u003eExpert consultation is necessary for scale development and revision. The scale\u0026rsquo;s four dimensions were finalized through expert consultation and literature references, namely, etiology and epidemiology, treatment, symptoms, and associated behaviors, as well as assessment and diagnosis\u003csup\u003e[46]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe expert consultation scale is based mainly on the scale\u0026rsquo;s accuracy, retention status, applicability to China, and dimensions. The ASKSP-R Scale (Revised Chinese Version) was revised after discussions with core group members and experts as well as a literature search. The ASKSP-R Scale (Revised Chinese Version) was revised to respect actual clinical practice. The final version has 25 items.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Scale reliability\u003c/h2\u003e \u003cp\u003eOur results revealed that the scale\u0026rsquo;s internal consistency reliability coefficient was 0.885 and exceeded the threshold criterion of 0.8. This indicated that the scale had good internal consistency and was suitable as an ASD knowledge measurement tool for Chinese professionals. Moreover, all four dimensions' CRs were \u0026gt;\u0026thinsp;0.7, thereby validating the scale\u0026rsquo;s internal consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Scale validity\u003c/h2\u003e \u003cp\u003eIn terms of structural validity, the scale\u0026rsquo;s KMO value was 0.888, and Bartlett's test of sphericity also passed the test of significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating the suitability of the ASKSP-R scale (Chinese version) for factor analysis. Additionally, the fragmentation plot and the validated factor analysis results confirmed the scale\u0026rsquo;s multidimensional structure. These findings indicate that the ASKSP-R scale (Chinese version) has good validity. We used the expert consultation method and a rubble diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to analyze the scale\u0026rsquo;s factors and the dimensions of various entries. Four common factors were extracted from the exploratory factor analysis to form four dimensions, namely, etiology and epidemiology, symptoms and related behaviors, assessment and diagnosis, and treatment. The scale\u0026rsquo;s multidimensionality was confirmed by validated factor analysis. The X\u003csup\u003e2\u003c/sup\u003e/df (chi-square degrees of freedom ratio) was 9.862, which might be due to a more complex model as well as a larger sample size. However, this large value was within acceptable limits. The AVE, AVE square root, and CR values of each dimension were acceptable. However, the CR was \u0026gt;\u0026thinsp;0.7, indicating that the ASKSP-R scale (Chinese version) had an acceptable convergence effect and superior discriminant validity. Thus, the Chinese Revised version of the ASKSP-R might be a reliable and valid method for measuring ASD knowledge in the Chinese population. Hence, the overall model\u0026rsquo;s fit for etiology and epidemiology, treatment, symptoms, and related behaviors, as well as assessment and diagnosis, was good, with an acceptable convergent effect. This finding indicates that all four dimensions were correlated and distinguishable from each other, thereby denoting an ideal discriminant validity of the scale data.\u003c/p\u003e \u003cp\u003eThe ASKSP-R scale (Chinese version) was significantly and positively associated with the validity scale, indicating that the trend of the subjects' ASKSP-R scale (Chinese version) was consistent with that of the ASK-Q questionnaire (Chinese version). This indicated the scale\u0026rsquo;s validity and reliability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Item response theory\u003c/h2\u003e \u003cp\u003eThe \"treatment\" dimension\u0026rsquo;s CR was 0.66, and the discrimination scores were all \u0026lt;\u0026thinsp;1. This may be due to the high difficulty coefficients of the questions in this dimension (all the questions in the \"treatment\" dimension had difficulty coefficients\u0026thinsp;\u0026gt;\u0026thinsp;1), which made it difficult to differentiate between the subjects' knowledge levels. We hypothesize that the greater difficulty with questions 10 and 11 may be due to poor knowledge about equestrian therapy in China. Xiao et al. (2023) explored the effects of equestrian-assisted activities and therapies for individuals with ASD in a systematic review. Although equine therapy can significantly improve social and behavioral functioning in ASD children, the effects are inconsistent across various subdomains (e.g., social awareness, motivation, and stereotyped behaviors). Thus, the effectiveness of therapy in different cultural contexts should be further investigated. Although \"equine therapy\" is widely recognized as an emerging intervention for ASD in theory, its practical application and awareness are still low (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). In China, very few relevant studies exist, leading to a lack of ASD knowledge among relevant professionals. This was also confirmed in our subsequent expert consultation. Therefore, this may explain the high difficulty levels of the questions and the \"treatment\" dimension\u0026rsquo;s low degree of differentiation. Nonetheless, the scale\u0026rsquo;s overall differentiation was good, and the difficulty level was within the acceptable range. These findings suggested that the overall validity of the ASKSP-R (Chinese version) scale was good.\u003c/p\u003e \u003cp\u003eAlthough the discrimination level of the \"treatment\" dimension was low, the overall discrimination level of the scale was satisfactory, with acceptable difficulty levels. This suggests that the scale's overall validity is robust. The study intentionally avoided autism as a condition to be \u0026lsquo;treated\u0026rsquo; in a traditional medical sense. Instead, the concentration was on interventions and support strategies designed to enhance quality of life, facilitate skill development, and address cooccurring conditions that could impact functioning.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Research innovations and shortcomings\u003c/h2\u003e \u003cp\u003eThe primary innovation of our study lies in the successful adaptation of the ASKSP-R scale for the Chinese population, creating a knowledge assessment tool for Chinese ASD professionals through rigorous localization and reliability testing. This effort could address the research gap in this area and provide a solid scientific basis for future clinical applications and training assessments. The scale\u0026rsquo;s use can enhance ASD diagnosis and intervention measures in China, improve the knowledge of relevant professionals, and ultimately offer more effective support for individuals with ASD. Consequently, we demonstrated the applicability of the Chinese version of the ASKSP-R scale for assessing the knowledge of Chinese ASD professionals by validating its reliability and validity. However, several limitations in this study need to be addressed. First, despite efforts to collect a representative sample, geographical and gender diversity was limited because of the use of random and snowball sampling from selected hospitals. This might impact the scale\u0026rsquo;s generalizability to a broader population. Therefore, future studies should aim for a larger, multicenter sample to better validate the scale\u0026rsquo;s applicability. Second, the impact of different cultural contexts on ASD knowledge perceptions was not considered. Future research will examine the adaptability of the ASKSP-R scale in various cultural settings through cross-cultural studies. Additionally, the second sample included only 26.65% men, which led to an underrepresentation of this group. As the ASK-Q (Chinese version) is an ASD knowledge scale designed for the general population and has not been validated for professionals, differences in the knowledge dimensions and focus between the general population and professionals might account for some of the observed differences in results. Given the diverse professional roles of participants, there might be variability in the level of ASD knowledge and the specific challenges faced by different groups, such as medical professionals and special education teachers. While this diversity reflects the multidisciplinary nature of ASD management, future studies will explore the specific needs and knowledge gaps within each subgroup. This study included a diverse group of professionals involved in ASD management, ranging from medical practitioners to special education teachers. While this approach provides valuable insights into the multidisciplinary nature of ASD care, it also introduces potential biases due to differences in participants\u0026rsquo; roles and experiences. For example, professionals in specialist schools might primarily serve children with severe ASD, which could affect their perspectives and knowledge compared with those working in mainstream settings. Additionally, while the presurvey helped refine the scale, its small sample size might limit the generalizability of feedback to the broader participant population. Future studies should consider stratified sampling and larger presurvey groups to address these limitations.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn summary, the Chinese version of the ASKSP-R was revised as the first ASD knowledge scale for professionals in China. With strong reliability and validity, it serves as an evaluation tool to assess the knowledge of Chinese professionals regarding autism-related disorders. This study contributes to enhancing the knowledge and expertise of those working with ASD while also supporting the earlier identification of ASD patients by medical practitioners. This can significantly help ASD patients receive tailored rehabilitation programs, such as social skills training, speech therapy, and behavioral interventions, and customize these interventions early.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll methods in the study were carried out in accordance with relevant guidelines and regulations or in accordance with the Declaration of Helsinki. The studies involving human participants were reviewed and approved by Guizhou Nursing Vocational College Ethics Review Committee. All participants signed an informed consent form before participation and had the option of withdrawing at any time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Consent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe manuscript does not include any detailed information, images or video material relating to individuals, so consent for publication is not applicable for the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Availability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors on request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by the Guizhou nursing vocational college (No. Gzhly2023-03)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Competing interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no competing interests to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Authors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, Yueying Zhang; Feng Hong; Zhujun Zhao and Yang Hong; methodology,Yueying Zhang; Fang Hou; software, Yueying Zhang; validation, Yueying Zhang, Yang Hong; formal analysis, Yueying Zhang; investigation, Yueying Zhang; Fudong Li; resources, Yueying Zhang; data curation, Yueying Zhang; writing\u0026mdash;original draft preparation, Yueying Zhang; writing\u0026mdash;review and editing, Yueying Zhang, Yang Hong; visualization, Yueying Zhang; supervision, Yang Hong; project administration, Yueying Zhang. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp; Acknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the author of the original scale, Dr. McClain, for her kind help and all the research participants for their participation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Clinical trial number:\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKanner L. Irrelevant and metaphorical language in early infantile autism. Am J Psychiatry. 1946;103(2):242-6.\u003c/li\u003e\n\u003cli\u003eAmerican Psychiatric Association D, American Psychiatric Association D. 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Pediatrics. 2015;136 Suppl 1(Suppl 1):S60-81.\u003c/li\u003e\n\u003cli\u003eHarstad E, Hanson E, Brewster SJ, DePillis R, Milliken AL, Aberbach G, et al. Persistence of Autism Spectrum Disorder From Early Childhood Through School Age. JAMA Pediatrics. 2023;177(11).\u003c/li\u003e\n\u003cli\u003eSun X, Allison C, Matthews FE, Zhang Z, Auyeung B, Baron‐Cohen S, et al. Exploring the Underdiagnosis and Prevalence of Autism Spectrum Conditions in Beijing. Autism Research. 2015;8(3):250-60.\u003c/li\u003e\n\u003cli\u003ePang Y, Lee CM, Wright M, Shen J, Shen B, Bo J. Challenges of case identification and diagnosis of Autism Spectrum Disorders in China: A critical review of procedures, assessment, and diagnostic criteria. Research in Autism Spectrum Disorders. 2018;53:53-66.\u003c/li\u003e\n\u003cli\u003eRice CE, Rosanoff M, Dawson G, Durkin MS, Croen LA, Singer A, et al. Evaluating Changes in the Prevalence of the Autism Spectrum Disorders (ASDs). 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J Autism Dev Disord. 2022;52(8):3676-85.\u003c/li\u003e\n\u003cli\u003eYu L, Stronach S, Harrison AJ. Public knowledge and stigma of autism spectrum disorder: Comparing China with the United States. Autism. 2020;24(6):1531-45.\u003c/li\u003e\n\u003cli\u003eJenks E, Selman F, Harmens M, Boon S, Tran T, Hobson H, et al. Teaching higher education staff to understand and support autistic students: evaluation of a novel training program. Frontiers in Psychiatry. 2023;14.\u003c/li\u003e\n\u003cli\u003eGu L, Ye C, He S, Deng C, Chen X, Liao Y, et al. Zhongshan city kindergarten care staff autism. Chinese Journal of School Health. 2016;37(11).\u003c/li\u003e\n\u003cli\u003eZhang L, Huo J, Gao L, Hao J, Yang W, Yu Y. Analysis of Awareness and Management Attitude towards Autism Spectrum Disorders among Different Populations. Health Medicine Research and Practice. 2019;16(2):23-6.\u003c/li\u003e\n\u003cli\u003eWang J, Wu J, Yang F, Zhou Y, Sun C, Liang S, et al. Survey of Autism Awareness in Caregivers of Children Aged 3 ~ 6 Years. Chinese Mental Health Journal. 2013;27(6):451-6.\u003c/li\u003e\n\u003cli\u003eHarrison AJ, Naqvi NC, Smit AK, Kumar PN, Muhammad NA, Saade S, et al. Assessing Autism Knowledge Across the Global Landscape Using the ASK-Q. Journal of Autism and Developmental Disorders. 2023;54(5):1897-911.\u003c/li\u003e\n\u003cli\u003eRafiei M, Nakhostin-Ansari A, Meshkat S, Khosravi A, Memari AH. Public awareness and stigma of autism spectrum disorder in Iran; An online survey. Research in Developmental Disabilities. 2023;134:104441.\u003c/li\u003e\n\u003cli\u003eMcClain MB, Harris B, Haverkamp CR, Golson ME, Schwartz SE. The ASKSP Revised (ASKSP-R) as a Measure of ASD Knowledge for Professional Populations. J Autism Dev Disord. 2020;50(3):998-1006.\u003c/li\u003e\n\u003cli\u003eHarrison AJ, Bradshaw LP, Naqvi NC, Paff ML, Campbell JM. Development and Psychometric Evaluation of the Autism Stigma and Knowledge Questionnaire (ASK-Q). J Autism Dev Disord. 2017;47(10):3281-95.\u003c/li\u003e\n\u003cli\u003eNohra J, Sacre Y, Abdel-Nour A, Mannan H, Khajuria D. Evaluation of Knowledge, Attitudes, and Practices Related to Osteoporosis and Correlates of Perceived High Risk among People Living in Two Main Districts of Lebanon. Journal of Osteoporosis. 2022;2022:1-8.\u003c/li\u003e\n\u003cli\u003eLiu B, Wen Z, Zhou L. Revision of the perioperative recovery scale for integrative medicine based on item response theory. Chinese Journal of Evidence-Based Medicine. 2024;24(4):439-44.\u003c/li\u003e\n\u003cli\u003eGore K, Gilbert M, Hawke M, Barbaro J. Investigating autism knowledge, self-efficacy, and confidence following maternal and child health nurse training for the early identification of autism. Frontiers in Neurology. 2024;14.\u003c/li\u003e\n\u003cli\u003eWaddington H, Shepherd D, van der Meer L, Powell-Hector N, Wilson E, Barbaro J. Brief Report: Training New Zealand Well Child/Tamariki Ora Nurses on Early Autism Signs Using the Social Attention and Communication Surveillance-Revised. Journal of Autism and Developmental Disorders. 2021;52(11):5050-7.\u003c/li\u003e\n\u003cli\u003eClarke L, Fung LK. The impact of autism-related training programs on physician knowledge, self-efficacy, and practice behavior: A systematic review. Autism. 2022;26(7):1626-40.\u003c/li\u003e\n\u003cli\u003eBen-Sasson A, Atun-Einy O, Yahav-Jonas G, Lev-On S, Gev T. Training Physical Therapists in Early ASD Screening. Journal of Autism and Developmental Disorders. 2018;48(11):3926-38.\u003c/li\u003e\n\u003cli\u003eBorgi M, Loliva D, Cerino S, Chiarotti F, Venerosi A, Bramini M, et al. Effectiveness of a Standardized Equine-Assisted Therapy Program for Children with Autism Spectrum Disorder. Journal of Autism and Developmental Disorders. 2015;46(1):1-9.\u003c/li\u003e\n\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 Spectrum Disorder, Knowledge, Autism Spectrum Knowledge Scale for Professional Version-Revised scale, Chinese, Professional population","lastPublishedDoi":"10.21203/rs.3.rs-6184251/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6184251/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eThis study aimed to translate, revise, and validate the Autism Spectrum Knowledge Scale for Professional Version-Revised (ASKSP-R), which is used to assess the knowledge of professionals involved in autism spectrum disorder (ASD) care and services, such as clinicians, educators, and therapists, in the Chinese context.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe used random and snowball sampling methods; the scale was revised via methods such as expert consultation and a literature search. We used EXCEL for descriptive analyses, SPSS27.0 for assessing the scale\u0026rsquo;s reliability and validity, and AMOS26 for validated factor analysis. Moreover, the 2PL model in item\u0026ndash;response theory (IRT) was analyzed for discrimination ability via R Studio (version 4.2.2).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eWith a Cronbach\u0026rsquo;s α of 0.885, the internal consistency of the ASKSP-R (Chinese version) was good. Moreover, a KMO of 0.888 indicated the scale\u0026rsquo;s superior validity, while its multidimensional structure was illustrated by RMSEA\u0026thinsp;=\u0026thinsp;0.059, IFI\u0026thinsp;=\u0026thinsp;0.88, TLI\u0026thinsp;=\u0026thinsp;0.866, and RFI\u0026thinsp;=\u0026thinsp;0.853. All the entries were between \u0026gt;\u0026thinsp;0.5 and \u0026lt;\u0026thinsp;3, and the difficulty levels ranged from \u0026minus;\u0026thinsp;3 to +\u0026thinsp;3. The AVE and CR values were \u0026gt;\u0026thinsp;0.36 and \u0026gt;\u0026thinsp;0.66, respectively.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe revised ASKSP-R scale has robust psychometric properties and can be used to assess the relevant knowledge of clinicians, educators, therapists, and other ASD-related professionals, enabling appropriate interventions on the basis of assessment results, promoting targeted training and education, and increasing the rate of early diagnosis and intervention for ASD.\u003c/p\u003e","manuscriptTitle":"Localization of the Autism Spectrum Disorder Knowledge Scale Professional Version (ASKSP-R) in Western Cities of China: A Case Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-14 23:25:14","doi":"10.21203/rs.3.rs-6184251/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":"1ea0b54f-0e0e-4c69-95da-d3176f349a60","owner":[],"postedDate":"April 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-08-12T13:38:48+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-14 23:25:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6184251","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6184251","identity":"rs-6184251","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}