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This study evaluated a parent-delivered social and emotional skills intervention--the Secret Agent Society (SAS) for children with high-functioning autism spectrum disorders (HF-ASD). The study was a pre-post follow-up design with an 8-week baseline period and 6-week follow-up period. Participants were 38 parents and 41 children recruited from regional/rural Queensland and metropolitan Brisbane, Australia. Child participants completed measures of social skills and emotion management, and parents completed measures related to child behavioral problems, parent self-efficacy, child anxiety, and parent emotional distress at pre-intervention, post intervention, and 6-week follow-up. Analyses of outcomes were conducted with a series of repeated-measures MANOVAs and one-way ANOVAs at post intervention and 6-week follow-up. There were significant improvements in child social skills reported by parents with gains maintained at 6-week follow-up with large effect sizes. Parent self-efficacy, child behavior, and child anxiety levels also improved significantly. In addition, outcomes from the SAS self-directed program were compared with the original clinic-based program conducted by Beaumont and Sofronoff. Results indicated greater changes in social skills outcomes in the clinic-based program and no difference between groups on emotional management strategies. The limitations of the study and clinical implications are discussed.

BackgroundPatient Priorities Care (PPC) is a model of care that aligns health care recommendations with priorities of older adults who have multiple chronic conditions. Following identification of patient priorities, this information is documented in the patient’s electronic health record (EHR). ObjectiveOur goal is to develop and validate a natural language processing (NLP) model that reliably documents when clinicians identify patient priorities (ie, values, outcome goals, and care preferences) within the EHR as a measure of PPC adoption. MethodsThis is a retrospective analysis of unstructured National Veteran Health Administration EHR free-text notes using an NLP model. The data were sourced from 778 patient notes of 658 patients from encounters with 144 social workers in the primary care setting. Each patient’s free-text clinical note was reviewed by 2 independent reviewers for the presence of PPC language such as priorities, values, and goals. We developed an NLP model that utilized statistical machine learning approaches. The performance of the NLP model in training and validation with 10-fold cross-validation is reported via accuracy, recall, and precision in comparison to the chart review. ResultsOf 778 notes, 589 (75.7%) were identified as containing PPC language (kappa=0.82, P