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Background/objectives: This study aimed to examine the reproducibility of food frequency questionnaires (FFQs) designed for young female nurses in the Korea Nurses' Health Study.
Subjects/methods: The reproducibility of web-based, self-administered FFQs was evaluated among 243 Korean female nurses. The first FFQ (FFQ1) was administered from March 2014 to February 2019 and the second FFQ (FFQ2) from November 2019, with a mean interval of 2.8 years between the FFQs (range, 9 months-5.6 years). Pearson and Spearman correlation coefficients (r values) and quartile agreements between FFQ1 and FFQ2 were calculated for intakes of energy, nutrients, and foods.
Results: Pearson correlation coefficients ranged from 0.41 to 0.55 (median r = 0.51) for energy and raw nutrients and from 0.16 to 0.46 (median r = 0.36) for energy-adjusted nutrients. Spearman correlation coefficients ranged from 0.25 to 0.72 (median r = 0.41) for food items. The percentages of women who were classified into the same or adjacent quartile were 77% to 84% (median = 82%) for raw nutrients and 69% to 86% (median = 78%) for foods.
Conclusions: The results indicated that the web-based FFQ used in the Korea Nurses' Health Study has acceptable reproducibility.
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http://dx.doi.org/10.4162/nrp.2022.16.1.106 | DOI Listing |
J Midwifery Womens Health
September 2025
College of Nursing, Research Institute of Nursing Innovation, Kyungpook National University, Daegu, South Korea.
Introduction: Given the rising number of studies on synthetic osmotic dilators, there is a lack of comprehensive reviews for their use compared with other commonly used cervical ripening methods. This study aimed to examine the maternal and neonatal safety and efficacy in cervical ripening and labor induction using synthetic osmotic dilators compared with pharmacologic agents (prostaglandin E, prostaglandin E, oxytocin) for labor induction.
Methods: A systematic review and meta-analysis of randomized controlled trials (RCTs) and cohort studies was conducted, using MEDLINE, Embase, CINAHL, and Cochrane Library databases search.
Asia Pac J Oncol Nurs
December 2025
Red Cross College of Nursing, Chung-Ang University, Seoul, Republic of Korea.
Objective: The lack of a clear and unified definition of shared decision-making (SDM) may hinder its effective application in oncology care. This study aims to clarify the concept of SDM specifically in the context of early-stage breast cancer treatment through an evolutionary concept analysis.
Methods: A systematic search was conducted across PubMed, CINAHL, PsycINFO, Cochrane, and EMBASE databases for articles published from January 2015 to December 2024.
J Hosp Palliat Care
September 2025
Emergency Department, Seoul National University Hospital, Seoul, Korea.
Purpose: This study aimed to identify predictors of end-of-life (EOL) care provided by emergency nurses in South Korea.
Methods: A cross-sectional survey was conducted using a structured questionnaire. Data were collected using Google Forms between June 21 and 30, 2022.
Asian Nurs Res (Korean Soc Nurs Sci)
September 2025
Daejeon Eulji University Hospital, Daejeon, South Korea. Electronic address:
Purpose: In this study, we aimed to develop and test the validity and reliability of the Korean version of the Novice Nursing Practitioner Role Transition (K-NNPRT) scale.
Methods: This scale was developed through forward translation, expert panel endorsement, and back translation and revised based on cognitive interviews. Data for the psychometric test were collected from 248 nurses who provide advanced care in Korea.
Nurse Educ Pract
August 2025
Department of Nursing, Mokpo National University, Muan-gun, Jeollanam-do, Republic of Korea. Electronic address:
Aim: This study aims to develop and validate an instructional debriefing model that combines question-centered learning methodology with AI prompt engineering techniques for nursing simulations.
Background: Integrating artificial intelligence (AI)-based prompt engineering into nursing simulation offers structured strategies to enhance clinical reasoning. However, current debriefing models insufficiently incorporate AI methodologies such as question-centered learning and prompt engineering, indicating a lack of theoretical and procedural frameworks METHODS: The model was developed using a four-phase approach: (1) literature review, (2) instructor interviews, (3) expert validation and (4) external evaluation of effectiveness.