Purpose: Clinical nurses who are mothers of preschool-aged children experience extreme parenting stress linked to their hospital work environment and shift work, differing from that generally experienced by mothers. This study aimed to develop and validate a parenting stress scale that considers the clinical nurses’ form of work and its characteristics. Methods: The scale items were initially derived from in-depth interviews and a literature review and were revised and modified based on the results of content validity testing by experts. The developed instrument was evaluated using data from 157 clinical nurses in South Korea who were mothers of preschool-aged children. Results: In the instrument validation stage, 19 items categorized in four factors (psychological burden, physical and mental fatigue, work shift, and work environment) were derived from construct validity, and the cumulative explanatory power was 56.6%. Furthermore, the convergent and discriminant validity and external construct were confirmed. Cronbach’s α of the final instrument was .86 (range: .81–.86). The validity and reliability of the newly developed parenting stress scale for clinical nurses were established in this study; it uses a 4-point Likert scale. A higher mean score by factor indicates a higher level of parenting stress experienced by clinical nurses. Conclusion This instrument would be beneficial to measure the level of parenting stress among nurses who work in hospitals and evaluate factors related to their parenting stress to devise effective interventions.

Purpose: High-dose radiotherapy (RT) for localized prostate cancer requires careful consideration of target position changes and adjacent organs-at-risk (OARs), such as the rectum and bladder. Therefore, daily monitoring of target position and OAR changes is crucial in minimizing interfractional dosimetric uncertainties. For efficient monitoring of the internal condition of patients, we assessed the feasibility of an auto-segmentation of OARs on the daily acquired images, such as megavoltage computed tomography (MVCT), via a commercial artificial intelligence (AI)-based solution in this study. Materials and Methods: We collected MVCT images weekly during the entire course of RT for 100 prostate cancer patients treated with the helical TomoTherapy system. Based on the manually contoured body outline, the bladder including prostate area, and rectal balloon regions for the 100 MVCT images, we trained the commercially available fully convolutional (FC)-DenseNet model and tested its auto-contouring performance. Results: Based on the optimally determined hyperparameters, the FC-DenseNet model successfully auto-contoured all regions of interest showing high dice similarity coefficient (DSC) over 0.8 and a small mean surface distance (MSD) within 1.43 mm in reference to the manually contoured data. With this well-trained AI model, we have efficiently monitored the patient's internal condition through six MVCT scans, analyzing DSC, MSD, centroid, and volume differences. Conclusion We have verified the feasibility of utilizing a commercial AI-based model for auto-segmentation with low-quality daily MVCT images. In the future, we will establish a fast and accurate auto-segmentation and internal organ monitoring system for efficiently determining the time for adaptive replanning.