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BACKGROUND: Postpartum depression (PPD) is a significant mental health concern affecting mothers globally. However, research on PPD prevalence and risk factors in Najran City, Saudi Arabia, is limited. STUDY AIM: this cross-sectional study aimed to determine the prevalence and risk factors associated with PPD among mothers in Najran City. METHODOLOGY: A questionnaire-based study was conducted from September 2023 to January 2024, involving 420 mothers aged 16-50 years with newborns (2-10 weeks after delivery). The questionnaire included demographic information and the Arabic version of the Edinburgh Postnatal Depression Scale (EPDS). Statistical analysis utilized SPSS software v. 26, including descriptive statistics, Mann-Whitney U test, Kruskal-Wallis H test, and logistic regression. RESULTS: The majority of participants were aged 20-35 years (61.4%), Saudi nationals (87.6%), and had university education (51.4%). EPDS scores indicated that 66.7% of mothers screened positive for possible depression. Significant associations were found between higher EPDS scores and factors such as unemployment (p = 0.004), younger age (p = 0.003), caesarean delivery (p = 0.043), mental illness (p = 0.0001), lack of adequate family support (p = 0.0001), and higher stress levels (p = 0.0001). CONCLUSION: The study revealed a high prevalence of PPD among mothers in Najran City, with sociodemographic, obstetric, and psychosocial factors significantly influencing PPD risk. These findings emphasize the need for targeted interventions and support systems to address maternal mental health needs effectively.
RESUMO
Background: As artificial intelligence makes rapid inroads across various fields, its value in medical education is becoming increasingly evident. This study evaluates the performance of the GPT-4.0 large language model in responding to plastic surgery board examination questions and explores its potential as a learning tool. Methods: We used a selection of 50 questions from 19 different chapters of a widely-used plastic surgery reference. Responses generated by the GPT-4.0 model were assessed based on four parameters: accuracy, clarity, completeness, and conciseness. Correlation analyses were conducted to ascertain the relationship between these parameters and the overall performance of the model. Results: GPT-4.0 showed a strong performance with high mean scores for accuracy (2.88), clarity (3.00), completeness (2.88), and conciseness (2.92) on a three-point scale. Completeness of the model's responses was significantly correlated with accuracy (P < 0.0001), whereas no significant correlation was found between accuracy and clarity or conciseness. Performance variability across different chapters indicates potential limitations of the model in dealing with certain complex topics in plastic surgery. Conclusions: The GPT-4.0 model exhibits considerable potential as an auxiliary tool for preparation for plastic surgery board examinations. Despite a few identified limitations, the generally high scores on key parameters suggest the model's ability to provide responses that are accurate, clear, complete, and concise. Future research should focus on enhancing the performance of artificial intelligence models in complex medical topics, further improving their applicability in medical education.
