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This study purports that parental rejection and warmth are critical to the development of adolescent drug use, and investigates a model that also considers children's vulnerability and deviant peer affiliations. It tests mediation through the proximal risk factor of deviant peers. Poisson growth curve modeling was used to examine participants from the Canadian National Longitudinal Survey of Children and Youth (NLSCY; N = 2194) over 4 waves. Results indicated that parental rejection was positively related to drug use, whereas parental warmth was negatively related to it. Both effects were significant when child ADHD symptoms were taken into account. Parental rejection and warmth had differential effects over time. Deviant peer affiliations were positively associated with drug use, did not have a differential influence over time, and did not mediate the other effects. There was significant between-individual (level 2) variability in drug use. Results are discussed in light of adolescent perceptions of parent-child relationships.

Background: The proportion of Canadian youth seeking mental health support from an emergency department (ED) has risen in recent years. As EDs typically address urgent mental health crises, revisiting an ED may represent unmet mental health needs. Accurate ED revisit prediction could aid early intervention and ensure efficient healthcare resource allocation. We examine the potential increased accuracy and performance of graph neural network (GNN) machine learning models compared to recurrent neural network (RNN), and baseline conventional machine learning and regression models for predicting ED revisit in electronic health record (EHR) data., Methods: This study used EHR data for children and youth aged 4-17 seeking services at McMaster Children's Hospital's Child and Youth Mental Health Program outpatient service to develop and evaluate GNN and RNN models to predict whether a child/youth with an ED visit had an ED revisit within 30 days. GNN and RNN models were developed and compared against conventional baseline models. Model performance for GNN, RNN, XGBoost, decision tree and logistic regression models was evaluated using F1 scores., Results: The GNN model outperformed the RNN model by an F1-score increase of 0.0511 and the best performing conventional machine learning model by an F1-score increase of 0.0470. Precision, recall, receiver operating characteristic (ROC) curves, and positive and negative predictive values showed that the GNN model performed the best, and the RNN model performed similarly to the XGBoost model. Performance increases were most noticeable for recall and negative predictive value than for precision and positive predictive value., Conclusions: This study demonstrates the improved accuracy and potential utility of GNN models in predicting ED revisits among children and youth, although model performance may not be sufficient for clinical implementation. Given the improvements in recall and negative predictive value, GNN models should be further explored to develop algorithms that can inform clinical decision-making in ways that facilitate targeted interventions, optimize resource allocation, and improve outcomes for children and youth., (© 2024. The Author(s).)