Exploring trauma recovery in nurses: a text mining and thematic analysis based on Swanson’s theory of caring

This study employed a secondary qualitative analysis approach to analyze free-text responses from nurses who participated in IBTRNI sessions as part of a survey-based study in a previous study [13]. Text mining, which involves extracting meaningful patterns and insights from large volumes of unstructured text data through computational methods [20], was used to analyze session-based responses. Text mining has been widely applied in healthcare research, such as identifying smoking status from electronic health records or detecting adverse drug events through natural language processing [21, 22]. This study used text mining to extract keywords and generate word cloud visualizations, effectively identifying major patterns within the responses.

Additionally, thematic analysis was conducted to explore the deeper meanings and emotional changes embedded in the responses from each session. The qualitative approach adopted a constructivist paradigm [23] to understand participants’ experiences as shaped by personal meaning-making processes. This allowed for a comprehensive exploration of the emotional and psychological recovery process, recognizing subjective experiences as socially constructed.

Participants in this study included 102 general nurses. Initially, 112 nurses were recruited for the primary study, but the secondary analysis included only 102 nurses who completed the intervention. Participants were sourced from two prominent online nursing platforms in Korea. The first, “Meetings Representing Nurses,” is the country’s largest nursing community, boasting over 95,000 registered members and averaging approximately 12,700 daily active users. The second platform, “I am a Nurse,” includes around 35,000 registered members, with an average daily activity of 150 users.

The inclusion criteria (applied in the primary study) were: (1) aged 23–40, (2) self-score greater than 80% (64 points) on the Korean version of the PTSD Checklist for DSM-5 (PCL-5), which evaluates overall trauma symptoms across four symptom clusters, (3) ability to access the program through a mobile device, (4) understand the purpose of the study and voluntarily agree to participate, and (5) Participants were excluded if they had been diagnosed with a severe mental illness and were taking medication for its treatment, even if they met the PTSD score threshold (≥ 64 points).

The demographic characteristics of the participants are summarized in Table 1. The mean age of participants was 31.04 (SD = 4.50), ranging from 22 to 40. Females accounted for most participants (96.1%), and the distribution of religious affiliation was evenly split, with 50% identifying themselves as religious. Most participants were single (60.8%), with 38.2% married and 1.0% separated, widowed, or divorced. Regarding educational background, 75.5% had attained a college degree or higher, and 24.5% had completed graduate school. The vast majority (90.2%) were currently employed, with 33.3% having 5–10 years of work experience and 27.5% having ten or more years of experience. Additionally, regarding the type of organization, 45 (44.1%) worked in tertiary referral hospitals, 52 (50.0%) in hospitals, and 5 (4.9%) in other organizations, including schools and health centers.

The IBTRNI was developed by the primary research team using Swanson’s theoretical framework [14, 24]. The secondary analysis analyzed these sessions through session-specific questions, focusing on three key concepts: “Knowing,” “Doing For,” and “Enabling.” Knowing emphasized self-understanding and self-respect, Doing For focused on self-acceptance and releasing negative emotions, and Enabling centered on effective communication and stress management [13, 24].

Nurses participating in the study responded to these questions at the end of each session in the online program. Responses were provided through text entries, allowing participants to explore their thoughts and feelings in relation to the goals of individual sessions. The data were automatically collected and stored by the platform. Researchers communicated with the participants throughout the intervention by providing feedback after each session and offering ongoing emotional support. Researchers provided participants standardized response letters after each session to ensure consistent feedback and emotional support throughout the intervention. The research team carefully developed these letters to align with the program’s goals and minimize the influence of individual researcher biases. While researchers maintained open communication to support participants, all interactions adhered to predefined protocols to reduce the potential for subjective influence. At the end of each section, informed by Swanson’s Theory of Caring, the participants were allowed to express their experiences and reflections on how they had changed through structured reflective questions. These reflective questions were designed to encourage nurses to assess their emotional and psychological changes during recovery, allowing for personal and subjective insights. For a detailed overview of these theory-based questions, refer to Table 2.

A secondary analysis was conducted using data from a primary study [13]. Institutional Review Board (IRB) approval (IRB No. Y-2021-0790) from X-University (blinded for review) was obtained for the secondary analysis. Participants in the primary study were informed of the current study and provided consent for using their responses in secondary data analysis.

We employed text mining and thematic analysis to systematically analyze nurses’ responses. For text mining, we used R (version 4.2.2), a widely-recognized tool for processing unstructured data [25]. The KoNLP (Korean Natural Language Processing) package and NIADic dictionary are well-established tools for processing Korean text [17]; however, they may have limitations in capturing certain contextual nuances specific to this study’s dataset [18]. To ensure accurate analysis, the research team conducted a heuristic purification process. This method focuses on iterative refinement to enhance the reliability of key issue extraction. By repeating the heuristic purification process, analysts can improve the reliability of the extracted key issues and address contextual nuances and inconsistencies to derive meaningful insights [26]. Contextually significant terms that were not included in the dictionary (e.g., “I-message”) were incorporated based on the consensus of the research team, ensuring their relevance to the study objectives. Common data pre-processing steps were taken, including eliminating duplicate and meaningless words (e.g., copulas, adjectives, adverbs) by designating them as stop words. Words that did not contribute to the overall context or analysis, such as frequently occurring function words (e.g., “있다,” “하다”), were also excluded. In cases where contextually-significant terms did not appear in the dictionary (e.g., “I-message”), the research team reached a consensus to include them as unique terms based on their relevance to trauma recovery and emotional processing themes. The cleaned data was then visualized using ggplot, through bar graphs and word clouds, to identify key patterns and high-frequency keywords.

To complement the text mining analysis, thematic analysis followed the six-phase approach outlined by Braun and Clarke [19]. Text mining identified high-frequency words from the participants’ responses, and corresponding sentences were subsequently extracted for further qualitative analysis. The researchers familiarized themselves with the extracted sentences through repeated reviews. Three team members independently performed initial coding, systematically identifying meaningful text segments. The coding process facilitated using Excel, breaking down each response into meaningful units and assigning descriptive codes to capture recurring patterns. Subsequently, the researchers convened to resolve any discrepancies in coding, ensuring inter-coder reliability. Once a consensus was reached, the codes were grouped into overarching themes that accurately reflected nurses’ emotional and psychological experiences during the intervention. These broader themes were reviewed iteratively to ensure consistency and alignment with the study objectives. The thematic analysis was conducted within the framework of Swanson’s Caring Theory, which served as the basis for both intervention design and analysis of participants’ responses. This theoretical approach guided the coding and categorization of themes throughout the analysis. Swanson’s three key concepts—“Knowing,” “Doing For,” and “Enabling”—were employed to categorize participants’ responses, facilitating the evaluation of how well the intervention aligned with the intended theoretical goals.

This study explored the trauma recovery experiences of nurses who participated in the IBTRINI program. Participants’ free-text responses were analyzed using a combination of text mining and thematic analysis based on Swanson’s three key concepts: “Knowing,” “Doing For,” and “Enabling.” Fig. 1 illustrates the top 10 keywords identified at each stage of the program, representing the most frequently mentioned terms by the participants during each phase. In addition to bar charts, word clouds visually depict the relative frequency of these keywords.

The “Knowing” phase focused on enhancing self-awareness and recognizing personal strengths. Text mining identified keywords such as thoughts, strength, trifles, gratitude, and self-control, reflecting participants’ focus on their emotional state and personal development.

The participants reported an increased awareness of their emotional responses and thought patterns, which facilitated the recognition of the impact of negative thoughts on their daily lives.

“I always believed that I was strong, but through this program, I realized how emotionally vulnerable I was.”

Additionally, the participants expressed a newfound appreciation of their strengths and the smaller, often overlooked, positive aspects of their lives, which contributed to their emotional resilience.

“I now try to appreciate even the small things in my life, and that’s a huge change for me.”

The “Doing For” phase centered on emotional regulation and behavioral transformation. Text mining revealed keywords such as positive, change of thought, behavior, emotion, and experience, indicating participants’ efforts to manage emotional responses and modify their behaviors.

The participants learned strategies to manage negative emotions and shift their responses toward more positive outcomes. Emotional regulation was crucial for behavioral changes and stress management.

“I started focusing more on positive thoughts and how they influenced my emotions, which helped me manage stress.”

By practicing emotional regulation, participants gained better control over their behaviors, allowing them to handle stress effectively and respond proactively in challenging situations.

“Now, when I face stressful situations, I change my thoughts and behavior before reacting emotionally.”

In the “Enabling” phase, the participants developed improved communication skills and stress management techniques. Text mining identified keywords such as conversation, daily life management, situation, meditation, and communication, focusing on managing interpersonal relationships and daily stressors.

The participants reported significant improvements in their interpersonal relationships, facilitated by enhanced communication skills and effective stress management strategies. Meditation was frequently mentioned as a tool that helped participants regulate emotions and engage more positively with others.

“Through meditation, I’ve been able to control my emotions, and it’s made conversations with others much easier.”

“I’ve learned not to always be defensive in conversations, which has improved my relationships.”

Moreover, the participants reported better management of daily life and stress, which empowered them to easily navigate challenging situations.

“Stress used to overwhelm me, but now I manage it through meditation and self-care.”

“I’ve started managing my daily life effectively, and when stressful situations arise, I feel more in control.”

The combination of text mining and thematic analysis can be considered a robust methodological approach for examining the complex nature of trauma recovery [18]. Text mining efficiently identified key patterns across large datasets, while thematic analysis provided richer insights into the emotional and psychological transformations experienced by participants. The integration of these methods approaches validates the relevance of Swanson’s theory in nursing interventions and offers a scalable framework for investigating trauma recovery processes across diverse healthcare settings.

The findings of this study underscore the practical applicability of IBTRNI in supporting nurses’ psychological recovery from trauma. By fostering the development of stress management techniques and communication skills, the intervention enhanced emotional resilience and coping abilities, which are critical for restoring psychological balance and functionality disrupted by trauma. These improvements highlight the potential of such interventions to promote emotional stability and overall well-being during recovery.

Additionally, this study emphasizes the necessity of integrating psychological recovery and coping strategy training into nursing education curricula. Preparing nurses with the skills to effectively manage the psychological impacts of trauma will contribute to building a more resilient healthcare workforce. Future research should focus on adapting IBTRNI for implementation in diverse clinical and cultural contexts, maximizing its impact on trauma recovery and psychological well-being.