Parental Perception of Children's Mental Health During the Pandemic: Insights From an Italian Cross-Sectional Study.

BACKGROUND: This study explores the impact of the pandemic on children's mental health. It examined the understanding of parents regarding their children's mental condition and their ability to identify issues, 2 years post the outbreak of the COVID-19 pandemic. METHODS: Using a cross-sectional design, 507 Italian parents reported on their youngest child aged between 2 and 17, totaling 507 children. The outcomes focused on were parental perception of children's mental health deterioration, scores on the Strengths and Difficulties Questionnaire (SDQ) above the clinical cut-off, and parental under-recognition of mental health issues. Descriptive analyses and multivariable logistic regression models were executed (significance at p < .05). RESULTS: Parents were 88.1% women (median age 41 years, interquartile range [IQR] = 36-47). Their children were 50.3% female [median age 6 years (IQR = 4-11)]. The data revealed 21.1% of parents perceived a deterioration in their children's mental health, while 44.2% had SDQ scores above the cut-off. Parental under-recognition of mental issues was found in 20.1% of cases. Significant correlations were found between parental perception of deterioration, SDQ scores, and factors like parental mental distress and children's sleep issues. IMPLICATIONS: The findings suggest that schools and verified websites can serve as critical conduits for providing parents with reliable information. By promoting early identification and intervention, such mechanisms can help ensure mental health equity for children. CONCLUSIONS: The research highlights the effect of the pandemic on children's mental health and the issue of parental under-recognition. The results underscore the importance of public health initiatives that enhance mental health information accessibility and reliability for parents.

Exploring the potential of ChatGPT for clinical reasoning and decision-making: a cross-sectional study on the Italian Medical Residency Exam.

BACKGROUND: This study aimed to assess the performance of ChatGPT, a large language model (LLM), on the Italian State Exam for Medical Residency (SSM) test to determine its potential as a tool for medical education and clinical decision-making support. MATERIALS AND METHODS: A total of 136 questions were obtained from the official SSM test. ChatGPT responses were analyzed and compared to the performance of medical doctors who took the test in 2022. Questions were classified into clinical cases (CC) and notional questions (NQ). RESULTS: ChatGPT achieved an overall accuracy of 90.44%, with higher performance on clinical cases (92.45%) than on notional questions (89.15%). Compared to medical doctors' scores, ChatGPT performance was higher than 99.6% of the participants. CONCLUSIONS: These results suggest that ChatGPT holds promise as a valuable tool in clinical decision-making, particularly in the context of clinical reasoning. Further research is needed to explore the potential applications and implementation of large language models (LLMs) in medical education and medical practice.