RESUMO
BACKGROUND: The optimal treatment for gender dysphoria is medical intervention, but many transgender and nonbinary people face significant treatment barriers when seeking help for gender dysphoria. When untreated, gender dysphoria is associated with depression, anxiety, suicidality, and substance misuse. Technology-delivered interventions for transgender and nonbinary people can be used discretely, safely, and flexibly, thereby reducing treatment barriers and increasing access to psychological interventions to manage distress that accompanies gender dysphoria. Technology-delivered interventions are beginning to incorporate machine learning (ML) and natural language processing (NLP) to automate intervention components and tailor intervention content. A critical step in using ML and NLP in technology-delivered interventions is demonstrating how accurately these methods model clinical constructs. OBJECTIVE: This study aimed to determine the preliminary effectiveness of modeling gender dysphoria with ML and NLP, using transgender and nonbinary people's social media data. METHODS: Overall, 6 ML models and 949 NLP-generated independent variables were used to model gender dysphoria from the text data of 1573 Reddit (Reddit Inc) posts created on transgender- and nonbinary-specific web-based forums. After developing a codebook grounded in clinical science, a research team of clinicians and students experienced in working with transgender and nonbinary clients used qualitative content analysis to determine whether gender dysphoria was present in each Reddit post (ie, the dependent variable). NLP (eg, n-grams, Linguistic Inquiry and Word Count, word embedding, sentiment, and transfer learning) was used to transform the linguistic content of each post into predictors for ML algorithms. A k-fold cross-validation was performed. Hyperparameters were tuned with random search. Feature selection was performed to demonstrate the relative importance of each NLP-generated independent variable in predicting gender dysphoria. Misclassified posts were analyzed to improve future modeling of gender dysphoria. RESULTS: Results indicated that a supervised ML algorithm (ie, optimized extreme gradient boosting [XGBoost]) modeled gender dysphoria with a high degree of accuracy (0.84), precision (0.83), and speed (1.23 seconds). Of the NLP-generated independent variables, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) clinical keywords (eg, dysphoria and disorder) were most predictive of gender dysphoria. Misclassifications of gender dysphoria were common in posts that expressed uncertainty, featured a stressful experience unrelated to gender dysphoria, were incorrectly coded, expressed insufficient linguistic markers of gender dysphoria, described past experiences of gender dysphoria, showed evidence of identity exploration, expressed aspects of human sexuality unrelated to gender dysphoria, described socially based gender dysphoria, expressed strong affective or cognitive reactions unrelated to gender dysphoria, or discussed body image. CONCLUSIONS: Findings suggest that ML- and NLP-based models of gender dysphoria have significant potential to be integrated into technology-delivered interventions. The results contribute to the growing evidence on the importance of incorporating ML and NLP designs in clinical science, especially when studying marginalized populations.
RESUMO
BACKGROUND: Some children may encounter difficulties in processing sensory stimuli, which may affect their ability to participate in activities of daily living. Self-regulation abilities may also affect children on how to process different sensory experiences. The Sensory Processing and Self-Regulation Checklist (SPSRC) was developed as a single, parent-reported instrument for the examination of sensory processing and self-regulation difficulties in children. AIMS: This study is aimed at evaluating the psychometric properties of the SPSRC and examine the patterns of self-regulation and sensory processing in children with and without autism spectrum disorder (ASD). METHODS AND PROCEDURES: The contents of the SPSRC were validated by a group of experts, and a field test was subsequently conducted to examine the reliability and validity of this instrument in a sample of 997 typically developing children and 78 children with ASD. OUTCOMES AND RESULTS: The results of the validation and field test analyses suggest that the SPSRC exhibits high internal consistency, good intrarater reliability, and a valid ability to measure and discriminate sensory processing and self-regulation in children aged 3-8 years with and without ASD. CONCLUSIONS AND IMPLICATIONS: The current results supported the reliability and validity of SPSRC to assess a child's sensory processing and self-regulation performance in activities of daily living. The study findings warrant further investigation to compare the performance of the SPSRC with laboratory-based tests, as this would better elucidate sensory responsivity in children with sensory modulation disorders from both clinical and research perspectives.