Association of sexual assault history with traumatic childbirth and subsequent PTSD

Berman, Zohar; Thiel, Freya; Kaimal, Anjali J; Dekel, Sharon

doi:10.1007/s00737-021-01129-0

Cited by 7 publications

(2 citation statements)

References 13 publications

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“…This is particularly important for victims of sexual assault. Women with a history of sexual assault are at a greater risk for obstetric complications and unplanned cesarean sections during childbirth and are at a higher risk for childbirth-related posttraumatic stress disorder compared with women without a history of sexual assault (Berman et al, 2021).…”

Section: Original Articlementioning

confidence: 99%

Forensic Nurses' Understanding of Emergency Contraception Mechanisms

Downing

Avshman

Valentine

et al. 2023

Journal of Forensic Nursing

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Background An estimated 25,000 pregnancies result from sexual assault in the United States annually. Numerous professional healthcare organizations endorse offering emergency contraception (EC) as an integrated aspect of post-sexual-assault care. Lack of knowledge surrounding EC's mechanism of action, including misinterpreting ECs as abortifacients, might restrict patient access to this important healthcare option. Purpose We evaluated sexual assault nurse examiners' understanding of the mechanism of action of oral ECs levonorgestrel (LNG) and ulipristal acetate (UPA). Methods A cross-sectional survey of practicing sexual assault nurse examiners was conducted through the International Association of Forensic Nurses. Results Among 173 respondents, 96.53% reported they prescribed/dispensed EC at the time of medical forensic examinations. LNG was prescribed more frequently than UPA (57.80% vs. 38.2%, respectively). When asked if they agreed or disagreed if LNG and UPA can disrupt an established pregnancy, 83.2% selected disagree/strongly disagree for LNG versus 78.6% for UPA, which were not significantly different. When asked whether the Supreme Court ruling overturning Roe v. Wade would change their EC prescribing, 79.77% reported it will have no change, 6.94% said it would increase, and 12.72% reported they were unsure. Several commented they were concerned whether state laws would prohibit EC and at least one program stopped prescribing EC because of their state laws. Implications Addressing misinformation regarding EC's mechanism of action and increasing access to oral EC options after sexual assault have the potential to reduce the incidence of rape-related pregnancy.

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Section: Original Articlementioning

confidence: 99%

Forensic Nurses' Understanding of Emergency Contraception Mechanisms

Downing

Avshman

Valentine

et al. 2023

Journal of Forensic Nursing

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“…Accurate screening could be the first step in optimizing opportunities for effective interventions and allocating appropriate resources to targeted, at-risk individuals. 21 The challenges of postpartum mental health screening involve, in part, women's tendency to under-report their symptoms. 22 Concerns of shame, stigma, and forced separation from infants, as well as poor awareness, hinder screening.…”

Section: Introductionmentioning

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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