Journal of Korean Academy of Nursing

Minji Kim

3 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

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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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Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service: Minji Kim, Mona Choi, Yoosik Youm; J Korean Acad Nurs 2017;47(6):806-816. Published online January 15, 2017; DOI: https://doi.org/10.4040/jkan.2017.47.6.806

Abstract PDF

Abstract Purpose

As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis.

Methods

The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword ‘comprehensive nursing care service’ using Python. A morphological analysis was performed using KoNLPy. Nodes on a ‘comprehensive nursing care service’ cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network.

Results

A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, ‘nursing workforce’ and ‘nursing service’ were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were ‘National Health Insurance Service’ and ‘comprehensive nursing care service hospital.’ The nodes with the highest edge weight were ‘national health insurance,’ ‘wards without caregiver presence,’ and ‘caregiving costs.’ ‘National Health Insurance Service’ was highest in degree centrality.

Conclusion

This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

Citations

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