Positive and negative contact as predictors of attitudes toward law enforcement

Using intergroup contact theory (ICT), which posits that contact experiences with members of outgroups relate to attitudes toward those outgroups as a whole, the current study examines how positive and negative experiences with members of law enforcement predict general attitudes toward law enforcement. It specifically examines how attitudes toward individual members of law enforcement from contact experiences generalize to law enforcement as a whole, and how this generalization process is more or less effective when members of law enforcement are seen as more or less representative of law enforcement as a group (i.e., when law enforcement group membership is salient). I predicted that positive contact experiences with members of law enforcement would relate to positive attitudes toward those individuals, which in turn would predict positive attitudes toward law enforcement in general. However, this process should be more effective when the individuals from those experiences are seen as typical and representative of law enforcement. A similar process should occur for negative contact experiences, except that negative experiences would predict less favorable attitudes. To assess these relationships, I collected data from an online sample of Americans (N = 505) through Amazon Cloud Research. The primary predictions were mostly supported. While the relationship between contact experiences with members of law enforcement and attitudes toward those individuals was inconsistent across analyses, attitudes toward individual members of law enforcement strongly related to general attitudes toward law enforcement, and this depended on the degree to which those individuals were seen as typical and representative of law enforcement. This was true for positive and negative contact. These findings make theoretical contributions to ICT by examining negative contact in conjunction with group salience and have important implications for how law enforcement should interact with members of their communities

Pediatric sex estimation using AI-enabled ECG analysis: influence of pubertal development

Abstract AI-enabled ECGs have previously been shown to accurately predict patient sex in adults and correlate with sex hormone levels. We aimed to test the ability of AI-enabled ECGs to predict sex in the pediatric population and study the influence of pubertal development. AI-enabled ECG models were created using a convolutional neural network trained on pediatric 10-second, 12-lead ECGs. The first model was trained de novo using pediatric data. The second model used transfer learning from a previously validated adult data-derived algorithm. We analyzed the first ECG from 90,133 unique pediatric patients (aged ≤18 years) recorded between 1987–2022, and divided the cohort into training, validation, and testing datasets. Subgroup analysis was performed on prepubertal (0–7 years), peripubertal (8–14 years), and postpubertal (15–18 years) patients. The cohort was 46.7% male, with 21,678 prepubertal, 26,740 peripubertal, and 41,715 postpubertal children. The de novo pediatric model demonstrated 81% accuracy and an area under the curve (AUC) of 0.91. Model sensitivity was 0.79, specificity was 0.83, positive predicted value was 0.84, and the negative predicted value was 0.78, for the entire test cohort. The model’s discriminatory ability was highest in postpubertal (AUC = 0.98), lower in the peripubertal age group (AUC = 0.91), and poor in the prepubertal age group (AUC = 0.67). There was no significant performance difference observed between the transfer learning and de novo models. AI-enabled interpretation of ECG can estimate sex in peripubertal and postpubertal children with high accuracy