Diving into the Resolution Process: Parent's Reactions to Child's Diagnosis.

This research presents an in-depth observation of parental resolution regarding a child's diagnosis with special needs to help counsellors understand the complexities of parental coping. Sixty-two parents of children with Autism Spectrum Disorder/Intellectual Developmental Delay participated in a Reaction to the Diagnosis Interview and a semi-structured interview. Categorical analysis revealed that 59.7% of the parents had reached resolution, with approximately 40% emotional orientation, 40% cognitive orientation, and 20% proactive orientation. Content analysis revealed three themes: emotions-feelings of guilt, shame, and emotional breakdown; thoughts-fear of stigma and concern for the child's future; actions-concealment, seeking support, and attempts to reject the results of the diagnosis. Whereas most parents were diagnosed as having reached resolution, the content analysis still found complex subject matter suggesting lack of resolution. Research findings show that counsellors should identify the intricate emotional dynamics of parents coping while being cautious of premature coping categorization.

Using categorical data analyses in suicide research: Considering clinical utility and practicality.

OBJECTIVE: Categorical data analysis is relevant to suicide risk and prevention research that focuses on discrete outcomes (e.g., suicide attempt status). Unfortunately, results from these analyses are often misinterpreted and not presented in a clinically tangible manner. We aimed to address these issues and highlight the relevance and utility of categorical methods in suicide research and clinical assessment. Additionally, we introduce relevant basic machine learning methods concepts and address the distinct utility of the current methods. METHOD: We review relevant background concepts and pertinent issues with references to helpful resources. We also provide non-technical descriptions and tutorials of how to convey categorical statistical results (logistic regression, receiver operating characteristic [ROC] curves, area under the curve [AUC] statistics, clinical cutoff scores) for clinical context and more intuitive use. RESULTS: We provide comprehensive examples, using simulated data, and interpret results. We also note important considerations for conducting and interpreting these analyses. We provide a walk-through demonstrating how to convert logistic regression estimates into predicted probability values, which is accompanied by Appendices demonstrating how to produce publication-ready figures in R and Microsoft Excel. CONCLUSION: Improving the translation of statistical estimates to practical, clinically tangible information may narrow the divide between research and clinical practice.