Journal of Korean Academy of Nursing

Juyoung Ha

2 Articles

Factors Influencing Oncofertility in Gynecological Cancer Patients: Application of Mixed Methods Study: Minji Kim, Juyoung Ha; J Korean Acad Nurs 2024;54(3):418-431. Published online August 31, 2024; DOI: https://doi.org/10.4040/jkan.23151

Purpose
This study aimed to identify factors influencing oncofertility and to explore the oncofertility experiences of patients with gynecological cancer using quantitative and qualitative methods, respectively. Methods: An explanatory sequential mixed-methods study was conducted. The quantitative study involved 222 patients with gynecological cancer recruited from online cafes and hospitals. Data were analyzed using IBM SPSS Statistics 28. For qualitative research, eight patients with gynecological cancer were interviewed. Data were analyzed using theme analysis method. Results: Oncofertility performance was quantitatively assessed in 40 patients (18.0%). Factors that significantly affected oncofertility were fertility preservation awareness (odds ratio [OR] = 14.97, 95% confidence interval [CI]: 4.22~53.08), number of children planned before cancer diagnosis (OR = 6.08, 95% CI: 1.89~19.62; OR = 5.04, 95% CI: 1.56~16.29), monthly income (OR = 3.29, 95% CI: 1.23~8.86), social support (OR = 1.08, 95% CI: 1.01~1.17), and anxiety (OR = 0.79, 95% CI: 0.66~0.95). Qualitative results showed three theme clusters and eight themes: (1) themes for determinant factors affecting oncofertility selection: ‘desire to have children’ and ‘special meaning of the uterus and ovaries;’ (2) themes for obstructive factors affecting oncofertility selection: ‘fertility preservation fall behind priorities,’ ‘confusion caused by inaccurate information,’ and ‘my choice was not supported;’ (3) themes for support factors affecting oncofertility selection: ‘provide accurate and reasonable information about oncofertility,’ ‘addressing the healthcare gap,’ and ‘need financial support for oncofertility.’ Conclusion: Financial support, sufficient information, social support, and anxiety-relief interventions are required for oncofertility in patients with gynecological cancer.

Citations

Citations to this article as recorded by

Digital health interventions for oncofertility in female patients: a systematic review
Juyoung Ha, Minji Kim, Hyojin Park
Women's Health Nursing.2025; 31(2): 119. CrossRef

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Prediction Models of Mild Cognitive Impairment Using the Korea Longitudinal Study of Ageing: Hyojin Park, Juyoung Ha; J Korean Acad Nurs 2020;50(2):191-199. Published online April 30, 2020; DOI: https://doi.org/10.4040/jkan.2020.50.2.191

Abstract PDF

Purpose
The purpose of this study was to compare sociodemographic characteristics of a normal cognitive group and mild cognitive impairment group, and establish prediction models of Mild Cognitive Impairment (MCI).
Methods
This study was a secondary data analysis research using data from “the 4th Korea Longitudinal Study of Ageing” of the Korea Employment Information Service. A total of 6,405 individuals, including 1,329 individuals with MCI and 5,076 individuals with normal cognitive abilities, were part of the study. Based on the panel survey items, the research used 28 variables. The methods of analysis included a c2-test, logistic regression analysis, decision tree analysis, predicted error rate, and an ROC curve calculated using SPSS 23.0 and SAS 13.2.
Results
In the MCI group, the mean age was 71.4 and 65.8% of the participants was women. There were statistically significant differences in gender, age, and education in both groups. Predictors of MCI determined by using a logistic regression analysis were gender, age, education, instrumental activity of daily living (IADL), perceived health status, participation group, cultural activities, and life satisfaction. Decision tree analysis of predictors of MCI identified education, age, life satisfaction, and IADL as predictors.
Conclusion
The accuracy of logistic regression model for MCI is slightly higher than that of decision tree model. The implementation of the prediction model for MCI established in this study may be utilized to identify middle-aged and elderly people with risks of MCI. Therefore, this study may contribute to the prevention and reduction of dementia.

Citations

Citations to this article as recorded by

Nomogram for predicting changes in cognitive function in community dwelling older adults with mild cognitive impairment based on Korea Longitudinal Study of Ageing Panel Data: a retrospective study
Hyuk Joon Kim, Hye Young Kim
Journal of Korean Academy of Nursing.2025; 55(1): 50. CrossRef
Development of a neural network-based risk prediction model for mild cognitive impairment in older adults with functional disability
Deyan Liu, Yuge Tian, Min Liu, Shangjian Yang
BMC Public Health.2025;[Epub] CrossRef
Cognitive Dysfunction Prediction Model with Lifelog Dataset based on Random Forest and SHAP
Myeongjin Lee, Jongun Lee, Hanjun Lee
The Journal of Korean Institute of Information Technology.2024; 22(1): 1. CrossRef
Sociodemographic Factors Predict Incident Mild Cognitive Impairment: A Brief Review and Empirical Study
Shuyi Jin, Chenxi Li, Jiani Miao, Jingyi Sun, Zhenqing Yang, Xingqi Cao, Kaili Sun, Xiaoting Liu, Lina Ma, Xin Xu, Zuyun Liu
Journal of the American Medical Directors Association.2023; 24(12): 1959. CrossRef
Characteristics and Factors Associated with Cognitive Decline of Elderly with Mild Cognitive Impairment
Eul Hee Roh
Journal of Health Informatics and Statistics.2023; 48(3): 179. CrossRef
Detection and Intervention of Subjective Cognitive Decline in Pre-Alzheimer’s Disease
雅红何
International Journal of Psychiatry and Neurology.2022; 11(04): 65. CrossRef
Influencing Factors of Subjective Cognitive Impairment in Middle-Aged and Older Adults
Min Roh, Hyunju Dan, Oksoo Kim
International Journal of Environmental Research and Public Health.2021; 18(21): 11488. CrossRef

1,680 View
23 Download
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