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Although numerous studies examine the effectiveness of intensive behavioral intervention programs (EIBI) for young children with autism, few focus on the family aspect of the program. In particular, involvement of mothers in the program, which is strongly recommended, is the subject of only a small number of studies. The goal of this research is to identify the forms of involvement adopted by mothers whose child follows an EIBI program. Thirteen mothers of children with autism receiving EIBI services were questioned through semistructured interviews. The results demonstrate that maternal involvement can take various forms and that these forms may vary from those usually described in the literature. (Contains 2 tables, 1 figure and 2 notes.)

Abstract Objective: The authors evaluated the efficacy of the 2002 college-based National Alcohol Screening Day (NASD) by determining: (1) the demographic and clinical characteristics of the participants that were screened and (2) the degree to which those scoring at hazardous drinking levels received clinical intervention or were referred for further assessment or treatment. Participants: Of 45,368 participants, 23,334 were screened and 14,598 received some form of clinical intervention, defined as advice or referral. Methods: The authors conducted nonparametric and univariate analyses to test for statistical differences in demographics, clinical-characteristics, and interventions-as a function of-screening mode. Results: Nearly 34% of those screened in person had an AUDIT score of 8 or higher, indicating harmful or hazardous drinking. Of these, only 10% had ever undergone alcohol treatment. More than 58% of those screened online scored 8 or higher, and of this group, fewer than 6% had ever undergone alcohol treatment. Conclusions: These data suggest that the in-person event and the online interactive program associated with NASD are serving markedly different populations, particularly with regard to clinical indicators. (Contains 6 tables and 1 figure.)

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Key Points: Question: Can prediction of posthospitalization suicides be improved by adding information from clinical notes and public records to a standard machine learning model? Findings: In this prognostic study including 448 788 hospitalizations, performance of a machine learning model to predict suicides after Veteran Health Administration psychiatric hospital discharges improved significantly when including predictors extracted from clinical notes with natural language processing and from a social determinants of health public records database. The resulting model had positive net benefit for plausible decision thresholds over risk horizons between 3 and 12 months. Meaning: In this study, clinical notes and public records improved machine learning model prediction of suicides after psychiatric hospital discharges. This prognostic study evaluates whether machine learning model prediction for postdischarge suicide could be improved by adding information extracted from clinical notes and public records. Importance: The months after psychiatric hospital discharge are a time of high risk for suicide. Intensive postdischarge case management, although potentially effective in suicide prevention, is likely to be cost-effective only if targeted at high-risk patients. A previously developed machine learning (ML) model showed that postdischarge suicides can be predicted from electronic health records and geospatial data, but it is unknown if prediction could be improved by adding additional information. Objective: To determine whether model prediction could be improved by adding information extracted from clinical notes and public records. Design, Setting, and Participants: Models were trained to predict suicides in the 12 months after Veterans Health Administration (VHA) short-term (less than 365 days) psychiatric hospitalizations between the beginning of 2010 and September 1, 2012 (299 050 hospitalizations, with 916 hospitalizations followed within 12 months by suicides) and tested in the hospitalizations from September 2, 2012, to December 31, 2013 (149 738 hospitalizations, with 393 hospitalizations followed within 12 months by suicides). Validation focused on net benefit across a range of plausible decision thresholds. Predictor importance was assessed with Shapley additive explanations (SHAP) values. Data were analyzed from January to August 2022. Main Outcomes and Measures: Suicides were defined by the National Death Index. Base model predictors included VHA electronic health records and patient residential data. The expanded predictors came from natural language processing (NLP) of clinical notes and a social determinants of health (SDOH) public records database. Results: The model included 448 788 unique hospitalizations. Net benefit over risk horizons between 3 and 12 months was generally highest for the model that included both NLP and SDOH predictors (area under the receiver operating characteristic curve range, 0.747-0.780; area under the precision recall curve relative to the suicide rate range, 3.87-5.75). NLP and SDOH predictors also had the highest predictor class-level SHAP values (proportional SHAP = 64.0% and 49.3%, respectively), although the single highest positive variable-level SHAP value was for a count of medications classified by the US Food and Drug Administration as increasing suicide risk prescribed the year before hospitalization (proportional SHAP = 15.0%). Conclusions and Relevance: In this study, clinical notes and public records were found to improve ML model prediction of suicide after psychiatric hospitalization. The model had positive net benefit over 3-month to 12-month risk horizons for plausible decision thresholds. Although caution is needed in inferring causality based on predictor importance, several key predictors have potential intervention implications that should be investigated in future studies. [ABSTRACT FROM AUTHOR]