Visualization of unstructured personal narratives of perterm birth using text network analysis

Abstract

Purpose This study aimed to identify the components of preterm birth (PTB) through women’s personal narratives and to visualize clinical symptom expressions (CSEs). Methods The participants were 11 women who gave birth before 37 weeks of gestational age. Personal narratives were collected by interactive unstructured storytelling via individual interviews, from August 8 to December 4, 2019 after receiving approval of the Institutional Review Board. The textual data were converted to PDF and analyzed using the MAXQDA program (VERBI Software). Results The participants’ mean age was 34.6 (±2.98) years, and five participants had a spontaneous vaginal birth. The following nine components of PTB were identified: obstetric condition, emotional condition, physical condition, medical condition, hospital environment, life-related stress, pregnancy-related stress, spousal support, and informational support. The top three codes were preterm labor, personal characteristics, and premature rupture of membrane, and the codes found for more than half of the participants were short cervix, fear of PTB, concern about fetal well-being, sleep difficulty, insufficient spousal and informational support, and physical difficulties. The top six CSEs were stress, hydramnios, false labor, concern about fetal wellbeing, true labor pain, and uterine contraction. “Stress” was ranked first in terms of frequency and “uterine contraction” had individual attributes. Conclusion The text network analysis of narratives from women who gave birth preterm yielded nine PTB components and six CSEs. These nine components should be included for developing a reliable and valid scale for PTB risk and stress. The CSEs can be applied for assessing preterm labor, as well as considered as strategies for students in women's health nursing practicum.

Keywords

• clinical presentation
• data visualization
• personal narrative
• premature obstetric labor