Journal of evaluation in clinical practice
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Domestic violence against women is a pervasive issue globally, representing a severe violation of human rights and a significant public health concern. The hidden nature of such violence and its frequent underreporting make it a critical area for research. Recent advancements in artificial intelligence offer new avenues for identifying and predicting instances of domestic violence through machine learning (ML) algorithms. ⋯ The findings of our study demonstrate that ML algorithms have high accuracy rates in determining the frequency and risk factors of domestic violence against women, indicating that they can be used safely.
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One of the factors affecting mothers' breastfeeding success is the father's support and the other is the mother's breastfeeding motivation. This study was conducted to determine the effect of partner breastfeeding support perceived by mothers and reported by fathers on mothers' breastfeeding motivation. ⋯ It was found that father support affected the autonomous motivation of both primiparous and multiparous mothers at a moderate level. Perceived partner breastfeeding support affected only the autonomous motivation of multiparous mothers at a moderate level, and the types of controlled motivation, namely infant health and social approval, at a weak level.
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This study was conducted to determine the effect of childhood adverse experiences on the risk of postpartum posttraumatic stress disorder (PP-PTSD) and postpartum depression (PPD). ⋯ The presence of ACE in mothers was found to increase the risk of developing PPD, both alone and when combined with traumatic birth experience. Therefore, we believe that screening for a history of ACE during pregnancy, investigating traumatic birth experiences in the postpartum period, closer follow-up of mothers with both ACE and traumatic birth experiences and increasing support systems will be beneficial in the prevention and early diagnosis of PPD.
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The previous studies demonstrated that the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system, a leading method for evaluating the certainty (quality) of scientific evidence (CoE), cannot reliably differentiate between various levels of CoE when the objective is to accurately assess the magnitude of the treatment effect. An estimated effect size is a function of multiple factors, including the true underlying treatment effect, biases, and other nonlinear factors that affect the estimate in different directions. We postulate that non-weighted, simple linear tallying can provide more accurate estimates of the probability of a true estimate of treatment effects as a function of CoE. ⋯ This study confirmed linear relationship between CoE and the probability of potentially 'true' estimates. We found that the probability of potentially "true" estimates decreases by about 20% for each drop in CoE (from about 80% for high to 55% for moderate to 35% to low and 15% to very low CoE).
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To investigate the effect of preadmission education given to laparoscopic sleeve gastrectomy patients on preoperative and postoperative anxiety, postoperative pain, and patient vital signs. ⋯ The education given to the patients before hospitalization decreased preoperative and postoperative anxiety levels, postoperative hospital stay and pain levels, and positively affected diastolic blood pressure, body temperature and saturation levels. One-to-one education given to patients in the outpatient clinic also contributes positively to their readiness for surgery. This study provides valuable evidence to the wider global clinical community by demonstrating the important benefits of preadmission education for patients undergoing bariatric surgery. Implementation of similar educational interventions in diverse healthcare settings worldwide may lead to increased postoperative recovery and improved overall patient well-being after bariatric surgery.