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The purpose of this project is to assess, for practicing pediatric nurses in the U.S., what is the impact of the Stewards of Children Child Sexual Abuse (CSA) program on their attitudes about reporting suspected CSA. A sample size of 32 nurses completed an online 2-hour continuing education course by Stewards of Children, with a pre/post-test survey. A modified 14-item version of the Teachers Reporting Attitudes Scale for Child Sexual Abuse (TRAS-CSA) was used to measure the nurses' attitudes before and after educational training. The surveys were analyzed to assess changes in attitudes using two-tailed sign tests. Nurses' commitment to reporting CSA is high, even before training. Nurses' confidence in the system of reporting and in the response of authorities related to CSA increased after taking the Stewards of Children online course. While these results are limited in strength by low sample sizes and some null changes, they indicate that trainings like the Stewards of Children course can positively impact nurses' attitudes towards reporting CSA, particularly with regards to confidence in reporting, and therefore warrant further investigation into CSA training for nurses and its effects. [ABSTRACT FROM AUTHOR]

Introduction: Limited evidence exists regarding medication administration errors (MAEs) on general paediatric wards or associated risk factors exists. Objective: The aim of this study was to identify nurse, medication, and work-environment factors associated with MAEs among paediatric inpatients. Methods: This was a prospective, direct observational study of 298 nurses in a paediatric referral hospital in Sydney, Australia. Trained observers recorded details of 5137 doses prepared and administered to 1530 children between 07:00 h and 22:00 h on weekdays and weekends. Observation data were compared with medication charts to identify errors. Clinical errors, potential severity and actual harm were assessed. Nurse characteristics (e.g. age, sex, experience), medication type (route, high-risk medications, use of solvent/diluent), and work variables (e.g. time of administration, weekday/weekend, use of an electronic medication management system [eMM], presence of a parent/carer) were collected. Multivariable models assessed MAE risk factors for any error, errors by route, potentially serious errors, and errors involving high-risk medication or causing actual harm. Results: Errors occurred in 37.0% (n = 1899; 95% confidence interval [CI] 35.7–38.3) of administrations, 25.8% (n = 489; 95% CI 23.8–27.9) of which were rated as potentially serious. Intravenous infusions and injections had high error rates (64.7% [n = 514], 95% CI 61.3–68.0; and 77.4% [n = 188], 95% CI 71.7–82.2, respectively). For intravenous injections, 59.7% (95% CI 53.4–65.6) had potentially serious errors. No nurse characteristics were associated with MAEs. Intravenous route, early morning and weekend administrations, patient age ≥ 11 years, oral medications requiring solvents/diluents and eMM use were all significant risk factors. MAEs causing actual harm were 45% lower using an eMM compared with paper charts. Conclusion: Medication error prevention strategies should target intravenous administrations and not neglect older children in hospital. Attention to nurses' work environments, including improved design and integration of medication technologies, is warranted. [ABSTRACT FROM AUTHOR]

This study aimed to assess the accuracy of liquid drug dose measurements made by caregivers and explore the factors influencing these measurements. Caregivers (n = 176) of children aged less than 8 years, who were treated at the pediatric clinic of a university hospital in Turkey between July and October 2019, were eligible to participate in this study. The caregivers' ability to accurately measure a 2.5-mL dose of medication was observed using standardized measurement instruments, including 15-mL and 30-mL dosing cups, a 3-mL dropper, a 5-mL dose spoon, and a 5-mL oral syringe. A comparison was made with the reference weight determined for the 2.5-mL dose to calculate the margin of error. A dose of ± 20% of the reference value was considered a clinically significant error. The chi-square test was used to examine differences in dose error rates concerning individual characteristics. Caregivers exhibited a dosing error rate exceeding 20%. Specifically, 43% of the errors occurred when using 15-mL cups, 37% with 30-mL cups, 22% with 3-mL droppers, 4.5% with 5-mL spoons, and 4% with 5-mL syringes. In cases where errors were under 20%, the rates were as follows: 1.1% with 15-mL cups, 2.8% with 30-mL cups, 19% with 3-mL droppers, 3.4% with 5-mL spoons, and 4% with 5-mL syringes. The dosing errors were not affected by the role and health literacy level of caregivers, regardless of the type of dosing tool they used (all p values > 0.05). The study found that oral syringes and dosing spoons had the lowest error rates, whereas dosing cups had the highest error rates. Conclusion: Healthcare providers in family health centers and pediatric clinics should educate caregivers about proper drug administration with oral syringes and dosing spoons, even if dosing cups are included. What is Known: • Dose calculation errors and incorrect measurement tools are the leading factors causing errors. • Liquid medicine bottles are still often accompanied by dosing cups as measuring instruments. • Both the American Academy of Pediatrics (AAP) and the U.S. Food and Drug Administration (FDA) recommend that parents use standard measuring instruments such as oral syringes, droppers, and measuring spoons instead of kitchen spoons for administering the correct dose to children. What is New: • The measuring tool with the maximum errors was the dosing cup, whereas oral syringes and dosing spoons were more accurate. • Individual administering medication at home and the health literacy level had no effect on the accuracy of dose measurement. • Pediatric nurses, in particular, should incorporate safe liquid medication measurement tools into parental education. [ABSTRACT FROM AUTHOR]

Artificial Intelligence (AI), particularly AI-Generated Imagery, has the potential to impact medical and patient education. This research explores the use of AI-generated imagery, from text-to-images, in medical education, focusing on congenital heart diseases (CHD). Utilizing ChatGPT's DALL·E 3, the research aims to assess the accuracy and educational value of AI-created images for 20 common CHDs. In this study, we utilized DALL·E 3 to generate a comprehensive set of 110 images, comprising ten images depicting the normal human heart and five images for each of the 20 common CHDs. The generated images were evaluated by a diverse group of 33 healthcare professionals. This cohort included cardiology experts, pediatricians, non-pediatric faculty members, trainees (medical students, interns, pediatric residents), and pediatric nurses. Utilizing a structured framework, these professionals assessed each image for anatomical accuracy, the usefulness of in-picture text, its appeal to medical professionals, and the image's potential applicability in medical presentations. Each item was assessed on a Likert scale of three. The assessments produced a total of 3630 images' assessments. Most AI-generated cardiac images were rated poorly as follows: 80.8% of images were rated as anatomically incorrect or fabricated, 85.2% rated to have incorrect text labels, 78.1% rated as not usable for medical education. The nurses and medical interns were found to have a more positive perception about the AI-generated cardiac images compared to the faculty members, pediatricians, and cardiology experts. Complex congenital anomalies were found to be significantly more predicted to anatomical fabrication compared to simple cardiac anomalies. There were significant challenges identified in image generation. Based on our findings, we recommend a vigilant approach towards the use of AI-generated imagery in medical education at present, underscoring the imperative for thorough validation and the importance of collaboration across disciplines. While we advise against its immediate integration until further validations are conducted, the study advocates for future AI-models to be fine-tuned with accurate medical data, enhancing their reliability and educational utility. [ABSTRACT FROM AUTHOR]