The Data Management of the Language of Trauma Narrative Communication

Chin, Shy Wei; Chong, Seng Tong; Musa, Abu Bakar B.; Loh, Kee Shyuan

doi:10.1109/educon46332.2021.9454015

Cited by 2 publications

(2 citation statements)

References 37 publications

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“…47 We examined the frequency of specific word categories in childbirth narratives that were previously shown to represent trauma narratives of individuals with PTSD. 32,48 Using LIWC, we examined the frequency of: 'Affect', 'Anger', 'Anx', 'Bio', 'Body', 'Cause', 'Cogproc', 'Death', 'Feel', 'Filler', 'Health', 'Hear', 'I', 'Insight', 'Negemo', 'Percept', 'Posemo', 'Pronoun', 'Sad', 'See', 'We', and 'You'. A Wilcoxon test for differences in the word frequency revealed that participants of the CB-PTSD class used fewer positive emotions, and more negative emotions as well as body-and death-related words in their birth narratives, compared with the No CB-PTSD class (Figure 2).…”

Section: Word Category Analysismentioning

confidence: 99%

Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives

Bartal

Jagodnik

Chan

et al. 2022

Preprint

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BackgroundMaternal mental disorders are considered a leading complication of childbirth and a common contributor to maternal death. In addition to undermining maternal welfare, untreated postpartum psychopathology can result in child emotional and physical neglect, and associated significant pediatric health costs. Some women may experience a traumatic childbirth and develop posttraumatic stress disorder (PTSD) symptoms following delivery (CB-PTSD). Although women are routinely screened for postpartum depression in the U.S., there is no recommended protocol to inform the identification of women who are likely to experience CB-PTSD. Advancements in computational methods of free text has shown promise in informing diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with post-trauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for CB-PTSD screening is unknown.ObjectiveThis study examined the utility of written narrative accounts of personal childbirth experience for the identification of women with provisional CB-PTSD. To this end, we developed a model based on natural language processing (NLP) and machine learning (ML) algorithms to identify CB-PTSD via classification of birth narratives.Study DesignA total of 1,127 eligible postpartum women who enrolled in a study survey during the COVID-19 era provided short written childbirth narrative accounts in which they were instructed to focus on the most distressing aspects of their childbirth experience. They also completed a PTSD symptom screen to determine provisional CB-PTSD. After exclusion criteria were applied, data from 995 participants was analyzed. An ML-based Sentence-Transformer NLP model was used to represent narratives as vectors that served as inputs for a neural network ML model developed in this study to identify participants with provisional CB-PTSD.ResultsThe ML model derived from NLP of childbirth narratives achieved good performance: AUC 0.75, F1-score 0.76, sensitivity 0.8, and specificity 0.70. Moreover, women with provisional CB-PTSD generated longer narratives (t-test results: t=2.30, p=0.02) and used more negative emotional expressions (Wilcoxon test: ‘sadness’: p=8.90e-04, W=31,017; ‘anger’: p=1.32e-02, W=35,005.50) and death-related words (Wilcoxon test: p=3.48e-05, W=34,538) in describing their childbirth experience than those with no CB-PTSD.ConclusionsThis study provides proof of concept that personal childbirth narrative accounts generated in the early postpartum period and analyzed via advanced computational methods can detect with relatively high accuracy women who are likely to endorse CB-PTSD and those at low risk. This suggests that birth narratives could be promising for informing low-cost, non-invasive tools for maternal mental health screening, and more research that utilizes ML to predict early signs of maternal psychiatric morbidity is warranted.

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Section: Word Category Analysismentioning

confidence: 99%

Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives

Bartal

Jagodnik

Chan

et al. 2022

Preprint

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“…Although there are many other examples of serious flooding in Malaysia in recent years, the most recent one occurred on 21st June 2020 during the Recovery Movement Control Order (RMCO) of Malaysia, in battling the Covid-19 pandemic, in five districts in Johor with 1,210 victims from 288 families to be evacuated to relief centres (Free Malaysia Today, 21 Jun 2020). However, the victims' voices or narratives are often unheard or even neglected (Chin et al, 2021;Chong et al, 2020).…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Narrative Analysis among B40 Students: From the Perspective of Flood-Related Trauma Analysis

Jin,

Tong,

Kiat

et al. 2023

Advances in Social Science, Education and Humanities Research

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The event of flooding and flood disasters across many countries is becoming more frequent or prone and at times the impact could be too devastating for any recovery. Malaysia has been regarded as a vulnerable country that needs to respond to floods more holistically which involves various stakeholders. The narratives of the affected people are often unheard or neglected. A narrative includes intentionally chosen, organized, and linked events that individuals think are important for a specific group of people. In this research, the use of trauma narrative analysis to look at the feedback from the affected people that might help society deal with the challenges of sustainable development, especially when it comes to communication management. In understanding narratives or feedback in the effort of contributing to trauma management, they could contribute to community self-efficacy and determine the dominant narratives. In summary, to ascertain the prevalent trauma narratives, it is established that respondents' voices are entirely consistent in stating that their current circumstances keep them 'on guard' with a high level of anxiety. Additionally, it is indicated that the respondents are aware of the dangers associated with residing near the flooding region. They are well prepared for any unanticipated tragedy, but they need to improve communication between themselves and local leaders and authorities to guarantee more involvement.

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The Data Management of the Language of Trauma Narrative Communication

Cited by 2 publications

References 37 publications

Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives

Identifying Women with Post-Delivery Posttraumatic Stress Disorder using Natural Language Processing of Personal Childbirth Narratives

Narrative Analysis among B40 Students: From the Perspective of Flood-Related Trauma Analysis

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