AI and narrative embeddings detect PTSD following childbirth via birth stories.

Free-text analysis using machine learning (ML)-based natural language processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.81) ChatGPT and six previously published large text-embedding models trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.

Systematic characterization of antibody-drug conjugate targets in central nervous system tumors.

BACKGROUND: Antibody-drug conjugates (ADCs) enhance the specificity of cytotoxic drugs by directing them to cells expressing target antigens. Multiple ADCs are FDA-approved for solid and hematologic malignancies, including those expressing HER2, TROP2, and NECTIN4. Recently, an ADC targeting HER2 (Trastuzumab-Deruxtecan) increased survival and reduced growth of brain metastases in treatment-refractory metastatic breast cancer, even in tumors with low HER2 expression. Thus, low-level expression of ADC targets may be sufficient for treatment responsiveness. However, ADC target expression is poorly characterized in many central nervous system (CNS) tumors. METHODS: We analyzed publicly available RNA-sequencing and proteomic data from the children's brain tumor network (N = 188 tumors) and gene-expression-omnibus RNA-expression datasets (N = 356) to evaluate expression of 14 potential ADC targets that are FDA-approved or under investigation in solid cancers. We also used immunohistochemistry to measure the levels of HER2, HER3, NECTIN4, TROP2, CLDN6, CLDN18.2, and CD276/B7-H3 protein in glioblastoma, oligodendroglioma, meningioma, ependymoma, pilocytic astrocytoma, medulloblastoma, atypical teratoid/rhabdoid tumor (AT/RT), adamantinomatous craniopharyngioma (ACP), papillary craniopharyngioma (PCP), and primary CNS lymphoma (N = 575). RESULTS: Pan-CNS analysis showed subtype-specific expression of ADC target proteins. Most tumors expressed HER3, B7-H3, and NECTIN4. Ependymomas strongly expressed HER2, while meningiomas showed weak-moderate HER2 expression. ACP and PCP strongly expressed B7-H3, with TROP2 expression in whorled ACP epithelium. AT/RT strongly expressed CLDN6. Glioblastoma showed little subtype-specific marker expression, suggesting a need for further target development. CONCLUSIONS: CNS tumors exhibit subtype-specific expression of ADC targets including several FDA-approved for other indications. Clinical trials of ADCs in CNS tumors may therefore be warranted.

OpenAI's Narrative Embeddings Can Be Used for Detecting Post-Traumatic Stress Following Childbirth Via Birth Stories.

Free-text analysis using Machine Learning (ML)-based Natural Language Processing (NLP) shows promise for diagnosing psychiatric conditions. Chat Generative Pre-trained Transformer (ChatGPT) has demonstrated preliminary initial feasibility for this purpose; however, whether it can accurately assess mental illness remains to be determined. This study evaluates the effectiveness of ChatGPT and the text-embedding-ada-002 (ADA) model in detecting post-traumatic stress disorder following childbirth (CB-PTSD), a maternal postpartum mental illness affecting millions of women annually, with no standard screening protocol. Using a sample of 1,295 women who gave birth in the last six months and were 18+ years old, recruited through hospital announcements, social media, and professional organizations, we explore ChatGPT's and ADA's potential to screen for CB-PTSD by analyzing maternal childbirth narratives. The PTSD Checklist for DSM-5 (PCL-5; cutoff 31) was used to assess CB-PTSD. By developing an ML model that utilizes numerical vector representation of the ADA model, we identify CB-PTSD via narrative classification. Our model outperformed (F1 score: 0.82) ChatGPT and six previously published large language models (LLMs) trained on mental health or clinical domains data, suggesting that the ADA model can be harnessed to identify CB-PTSD. Our modeling approach could be generalized to assess other mental health disorders.

Screening for post-traumatic stress disorder following childbirth using the Peritraumatic Distress Inventory.

BACKGROUND: Post-traumatic stress disorder (PTSD) following traumatic childbirth may undermine maternal and infant health, but screening for maternal childbirth-related PTSD (CB-PTSD) remains lacking. Acute emotional distress in response to a traumatic experience strongly associates with PTSD. The Peritraumatic Distress Inventory (PDI) assesses acute distress in non-postpartum individuals, but its use to classify women likely to endorse CB-PTSD is unknown. METHODS: 3039 women provided information about their mental health and childbirth experience. They completed the PDI regarding their recent childbirth event, and a PTSD symptom screen to determine CB-PTSD. We employed Exploratory Graph Analysis and bootstrapping to reveal the PDI's factorial structure and optimal cutoff value for CB-PTSD classification. RESULTS: Factor analysis revealed two strongly correlated stable factors based on a modified version of the PDI: (1) negative emotions and (2) bodily arousal and threat appraisal. A score of 15+ on the modified PDI produced high sensitivity and specificity: 88 % with a positive CB-PTSD screen in the first postpartum months and 93 % with a negative screen. LIMITATIONS: In this cross-sectional study, the PDI was administered at different timepoints postpartum. Future work should examine the PDI's predictive utility for screening women as closely as possible to the time of childbirth, and establish clinical cutoffs in populations after complicated deliveries. CONCLUSIONS: Brief self-report screening concerning a woman's emotional reactions to childbirth using our modified PDI tool can detect those likely to endorse CB-PTSD in the early postpartum. This may serve as the initial step of managing symptoms to ultimately prevent chronic manifestations.

2D and 3D multiplexed subcellular profiling of nuclear instability in human cancer.

Nuclear atypia, including altered nuclear size, contour, and chromatin organization, is ubiquitous in cancer cells. Atypical primary nuclei and micronuclei can rupture during interphase; however, the frequency, causes, and consequences of nuclear rupture are unknown in most cancers. We demonstrate that nuclear envelope rupture is surprisingly common in many human cancers, particularly glioblastoma. Using highly-multiplexed 2D and super-resolution 3D-imaging of glioblastoma tissues and patient-derived xenografts and cells, we link primary nuclear rupture with reduced lamin A/C and micronuclear rupture with reduced lamin B1. Moreover, ruptured glioblastoma cells activate cGAS-STING-signaling involved in innate immunity. We observe that local patterning of cell states influences tumor spatial organization and is linked to both lamin expression and rupture frequency, with neural-progenitor-cell-like states exhibiting the lowest lamin A/C levels and greatest susceptibility to primary nuclear rupture. Our study reveals that nuclear instability is a core feature of cancer, and links nuclear integrity, cell state, and immune signaling.

Screening for Post-Traumatic Stress Disorder following Childbirth using the Peritraumatic Distress Inventory.

Background: Maternal psychiatric morbidities include a range of psychopathologies; one condition is post-traumatic stress disorder (PTSD) that develops following a traumatic childbirth experience and may undermine maternal and infant health. Although assessment for maternal mental health problems is integrated in routine perinatal care, screening for maternal childbirth-related PTSD (CB-PTSD) remains lacking. Acute emotional distress in response to a traumatic event strongly associates with PTSD. The brief 13-item Peritraumatic Distress Inventory (PDI) is a common tool to assess acute distress in non-postpartum individuals. How well the PDI specified to childbirth can classify women likely to endorse CB-PTSD is unknown. Objectives: We sought to determine the utility of the PDI to detect CB-PTSD in the early postpartum period. This involved examining the psychometric properties of the PDI specified to childbirth, pertaining to its factorial structure, and establishing an optimal cutoff point for the classification of women with high vs. low likelihood of endorsing CB-PTSD. Study Design: A sample of 3,039 eligible women who had recently given birth provided information about their mental health and childbirth experience. They completed the PDI regarding their recent childbirth event, and a PTSD symptom screen to determine CB-PTSD. We employed Exploratory Graph Analysis (EGA) and bootstrapping analysis to reveal the factorial structure of the PDI and the optimal PDI cutoff value for CB-PTSD classification. Results: Factor analysis of the PDI shows two strongly correlated stable factors based on a modified 12-item version of the PDI consisting of (1) negative emotions and (2) bodily arousal and threat appraisal in regard to recent childbirth. This structure largely accords with prior studies of individuals who experienced acute distress resulting from other forms of trauma. We report that a score of 15 or higher on the modified PDI produces strong sensitivity and specificity. 88% of women with a positive CB-PTSD screen in the first postpartum months and 93% with a negative screen are identified as such using the established cutoff. Conclusions: Our work reveals that a brief self-report screening concerning a woman's immediate emotional reactions to childbirth that uses our modified PDI tool can detect women likely to endorse CB-PTSD in the early postpartum period. This form of maternal mental health assessment may serve as the initial step of managing symptoms to ultimately prevent chronic symptom manifestation. Future research is needed to examine the utility of employing the PDI as an assessment performed during maternity hospitalization stay in women following complicated deliveries to further guide recommendations to implement maternal mental health screening for women at high risk for developing CB-PTSD.

Re-examining content validity of the BREAST-Q more than a decade later to determine relevance and comprehensiveness.

PURPOSE: The BREAST-Q is the most used patient-reported outcome measure (PROM) in breast cancer surgery. The purposes of this study were to re-examine the content validity of BREAST-Q cancer modules (mastectomy, lumpectomy and reconstruction) and to determine the need for new scales. METHODS: Interviews were conducted with women with breast cancer (Stage 0-4, any treatment), and were audio-recorded and transcribed verbatim. Deductive (based on original BREAST-Q conceptual framework) and inductive (new codes from the data) content analysis approaches were used to analyze the data. The number of codes that mapped to BREAST-Q were recorded. RESULTS: Dataset included 3948 codes from 58 participants. Most of the breast (n = 659, 96%) and all psychosocial (n = 127, 100%), sexual (n = 179, 100%) and radiation-related (n = 79, 100%) codes mapped to BREAST-Q Satisfaction with Breast, Psychosocial Wellbeing, Sexual Wellbeing and Adverse Effects of Radiation scales, respectively. For the physical wellbeing codes (n = 939) for breast/chest and arm, 34% (n = 321) mapped to the Physical Wellbeing-Chest scale. Most of the abdomen codes (n = 311) mapped to Satisfaction with Abdomen (n = 90, 76%) and Physical Wellbeing-Abdomen (n = 171, 89%) scales. Codes that did not map (n = 697, 30%) covered breast sensation and lymphedema. Concerns related to fatigue, cancer worry, and work impact were most reported and did not map to BREAST-Q. CONCLUSION: The BREAST-Q, which was developed using extensive patient input more than a decade ago, is still relevant. To ensure the BREAST-Q remains comprehensive, new scales for upper extremity lymphedema, breast sensation, fatigue, cancer worry, and work impact were developed.

Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives.

BACKGROUND: Maternal 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 traumatic childbirth and develop posttraumatic stress disorder symptoms after delivery (childbirth-related posttraumatic stress disorder). Although women are routinely screened for postpartum depression in the United States, there is no recommended protocol to inform the identification of women who are likely to experience childbirth-related posttraumatic stress disorder. Advancements in computational methods of free text have shown promise in informing the diagnosis of psychiatric conditions. Although the language in narratives of stressful events has been associated with posttrauma outcomes, whether the narratives of childbirth processed via machine learning can be useful for childbirth-related posttraumatic stress disorder screening is unknown. OBJECTIVE: This study aimed to examine the use of written narrative accounts of personal childbirth experiences for the identification of women with childbirth-related posttraumatic stress disorder. To this end, we developed a model based on natural language processing and machine learning algorithms to identify childbirth-related posttraumatic stress disorder via the classification of birth narratives. STUDY DESIGN: Overall, 1127 eligible postpartum women who enrolled in a study survey during the COVID-19 pandemic 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 posttraumatic stress disorder symptom screen to determine childbirth-related posttraumatic stress disorder. After the exclusion criteria were applied, data from 995 participants were analyzed. A machine learning-based Sentence-Transformers natural language processing model was used to represent narratives as vectors that served as inputs for a neural network machine learning model developed in this study to identify participants with childbirth-related posttraumatic stress disorder. RESULTS: The machine learning model derived from natural language processing of childbirth narratives achieved good performance (area under the curve, 0.75; F1 score, 0.76; sensitivity, 0.8; specificity, 0.70). Moreover, women with childbirth-related posttraumatic stress disorder generated longer narratives (t test results: t=2.30; p=.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 childbirth-related posttraumatic stress disorder. CONCLUSION: This study provided 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 childbirth-related posttraumatic stress disorder and those at low risk. This suggests that birth narratives could be promising for informing low-cost, noninvasive tools for maternal mental health screening, and more research that used machine learning to predict early signs of maternal psychiatric morbidity is warranted.

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

Background: Maternal 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. Objective: This 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 Design: A 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. Results: The 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. Conclusions: This 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.

Traumatic childbirth during COVID-19 triggers maternal psychological growth and in turn better mother-infant bonding.

BACKGROUND: Although posttraumatic psychological growth (PTG) occurs following stressful events, knowledge of maternal psychological growth as a result of giving birth during the novel coronavirus (COVID-19) pandemic is lacking. METHODS: We assessed PTG associated with recent childbirth (Posttraumatic Growth Inventory) in a sample of 2205 women who gave birth during the pandemic and 540 who gave birth before. They also provided information about birth-related traumatic stress (Peritraumatic Distress Inventory; PTSD Checklist), mother-infant bonding (Maternal Attachment Inventory), and breastfeeding. RESULTS: Close to two thirds (60.45 %) of participants reported childbirth-related PTG with greater appreciation of life endorsed most frequently. No group differences in PTG prevalence were noted between deliveries during or before COVID-19 (χ2 = 0.35, p = 0.84). A multigroup mediation model revealed that in deliveries during the pandemic, childbirth-related acute stress was linked with elevated PTG (ß = 0.07, p < 0.01); in turn, PTG was associated with lower posttraumatic stress symptoms (ß = -0.06, p < 0.05) and better mother-infant bonding (ß = 0.22, p < 0.001). These indirect paths via PTG were not significant in deliveries before the pandemic. LIMITATIONS: Reliance on a convenient sample, self-reports, and cross-sectional design may introduce bias. CONCLUSIONS: Perceived positive maternal psychological changes as a result of childbirth are endorsed by a significant portion of women during the pandemic and can ensue in response to traumatic childbirth. Maternal growth is further implicated in successful postpartum adjustment and positive mother-infant interactions during an important period. Hence, directing clinical attention to opportunities of maternal psychological growth may have benefits especially for women at risk for the adverse outcomes of exposure to traumatic experiences of childbirth.

A Serious Game for Young People With First Episode Psychosis (OnTrack>The Game): Qualitative Findings of a Randomized Controlled Trial.

BACKGROUND: Several studies have shown the benefits of coordinated specialty care (CSC) for individuals with first episode psychosis; however, pathways to care are marred by lack of knowledge, stigma, and difficulties with treatment engagement. Serious games or video interventions may provide a way to address these factors. OBJECTIVE: This study focuses on qualitative results of a randomized controlled trial comparing OnTrack>The Game (OTG) with recovery videos (RVs) on engagement, stigma, empowerment, hope, recovery, and understanding of psychosis in clients receiving CSC. Clinicians are also interviewed regarding their perceptions of the interventions and suggestions for improvement. METHODS: A total of 16 clients aged 16-30 years, with first episode psychosis attending a CSC program in New York State, and 9 clinicians participated in the qualitative interviews. Interviews were analyzed using the rapid identification of themes from audio recordings method. RESULTS: For clients, themes included relatability of game content, an increased sense of hope and the possibility of recovery, decreased self-stigma and public stigma, increased understanding of the importance of social support, and increased empowerment in the OTG group. Clinicians had a preference for RV and provided suggestions for dissemination and implementation. CONCLUSIONS: Themes that may help inform future research in this area, particularly regarding dissemination and implementation of OTG and RV, emerged. TRIAL REGISTRATION: ClinicalTrials.gov NCT03390491; https://clinicaltrials.gov/ct2/show/NCT03390491.

Utilizing Instagram Data to Identify Usage Patterns Associated With Schizophrenia Spectrum Disorders.

Background and Objectives: Prior research has successfully identified linguistic and behavioral patterns associated with schizophrenia spectrum disorders (SSD) from user generated social media activity. Few studies, however, have explored the potential for image analysis to inform psychiatric care for individuals with SSD. Given the popularity of image-based platforms, such as Instagram, investigating user generated image data could further strengthen associations between social media activity and behavioral health. Methods: We collected 11,947 Instagram posts across 68 participants (mean age = 23.6; 59% male) with schizophrenia spectrum disorders (SSD; n = 34) and healthy volunteers (HV; n = 34). We extracted image features including color composition, aspect ratio, and number of faces depicted. Additionally, we considered social connections and behavioral features. We explored differences in usage patterns between SSD and HV participants. Results: Individuals with SSD posted images with lower saturation (p = 0.033) and lower colorfulness (p = 0.005) compared to HVs, as well as images showing fewer faces on average (SSD = 1.5, HV = 2.4, p < 0.001). Further, individuals with SSD demonstrated a lower ratio of followers to following compared to HV participants (p = 0.025). Conclusion: Differences in uploaded images and user activity on Instagram were identified in individuals with SSD. These differences highlight potential digital biomarkers of SSD from Instagram data.

COVID-19 positivity associated with traumatic stress response to childbirth and no visitors and infant separation in the hospital.

As the novel coronavirus (COVID-19) has spread globally, a significant portion of pregnant and delivering women were infected with COVID-19. While emerging studies examined birth outcomes in COVID-19 positive women, knowledge of the psychological experience of childbirth and maternal wellness remains lacking. This matched-control survey-based study included a sample of women recruited during the first wave of the pandemic in the US who gave birth in the previous six months. Women reporting confirmed/suspected COVID-19 (n = 68) during pregnancy or childbirth were matched on background factors with women reporting COVID-19 negativity (n = 2,276). We found nearly 50% of COVID positive women endorsed acute traumatic stress symptoms at a clinical level in response to childbirth. This group was more than twice as likely to endorse acute stress and to have no visitors during maternity hospitalization than COVID negative women; they were also less likely to room-in with newborns. The COVID positive group reported higher levels of pain in delivery, lower newborn weights, and more infant admission to neonatal intensive care units. Our findings suggest COVID-19 affected populations are at increased risk for traumatic childbirth and associated risk for psychiatric morbidity. Attention to delivering women's wellbeing is warranted during the pandemic.

COVID-19 is associated with traumatic childbirth and subsequent mother-infant bonding problems.

BACKGROUND: Knowledge of women's experience of childbirth in the outbreak of the coronavirus (COVID-19) pandemic and associated maternal health outcomes is scarce. METHODS: A sample of primarily American women who gave birth around the height of COVID-19 (n = 1,611) and matched controls, i.e., women who gave birth before COVID-19 (n = 640), completed an anonymous Internet survey about recent childbirth, birth-related traumatic stress (peritraumatic distress inventory; PTSD-checklist), maternal bonding (maternal attachment inventory; mother-to-infant bonding scale) and breastfeeding status. Groups (n = 637 in each) were matched on demographics, prior mental health/trauma and childbirth factors to determine the unique contribution of COVID-19 to the psychological experience of childbirth. RESULTS: Mothers in COVID-19-exposed communities endorsed more clinically acute stress response to childbirth than matched controls (Z = 2.65, p = .008, OR= 1.38). A path mediation model revealed that acute stress mediated the relationship between study group and postpartum outcomes. Specifically, higher acute stress response in birth was associated with more childbirth-related posttraumatic stress disorder symptoms (ß = .42, p < .001) and less bonding with the infant (ß = .26, p < .001), including breastfeeding problems (ß = .10, p < .01). LIMITATIONS: Use of a convenient internet sample introduces bias towards more educated women and reliance on retrospective self-report assessments may entail recall bias. CONCLUSIONS: COVID-19 is a major stressor for delivering women. It can heighten traumatic childbirth experiences and interfere with successful postpartum adjustment. Clinical attention to traumatic stress in childbirth and problems with caring for the young during this pandemic is important.

Traumatic memories of childbirth relate to maternal postpartum posttraumatic stress disorder.

The trauma memory is a crucial feature of PTSD etiology and maintenance. Nonetheless, the nature of memories associated with childbirth-related posttraumatic stress disorder (CB-PTSD) requires explication. The present study, as part of a larger project on psychological outcomes of childbirth, utilized a multi-method approach to characterize childbirth memories in relation to CB-PTSD symptoms. We here assessed 413 women who completed self-report measures pertaining to CB-PTSD, postpartum depression, and childbirth memories. Additionally, a subset of 209 women provided written childbirth narratives, analyzed using Linguistic Inquiry and Word Count software. Women endorsing CB-PTSD symptoms on the PTSD-Checklist (PCL)-5 reported more incoherent childbirth memories with more emotional and sensory details, and more frequent involuntary recall and reliving of the memory. They also indicated the childbirth experience was more central to their identity. Written narratives in those with probable CB-PTSD were characterized by less (positive) affective processes, and more cognitive processes. We infer that childbirth memories in women who endorse symptoms of CB-PTSD in the early postpartum period resemble those described in the general PTSD literature. This suggests that childbirth may be experienced as traumatic and evoke a traumatic memory, implicated in symptom endorsement. Opportunities for therapeutic interventions modifying traumatic memories of childbirth in women at risk for CB-PTSD need to be investigated. Future research examining characteristics of traumatic childbirth memories is needed to advance our understanding of this overlooked postpartum condition.

Maternal psychological growth following childbirth.

Although maternal postpartum mental health has been extensively studied, rather little is known regarding the factors that may facilitate psychological growth following childbirth. The present study set forth to examine various pre-birth, birth, and post-birth correlates of overall psychological growth and growth domains in postpartum women, assessed within the first months following childbirth. A sample of 428 women completed self-report measures pertaining to psychological growth, mental health, maternal attachment, and childbirth characteristics. We found that the majority of women reported psychological growth following childbirth, with those experiencing stressors in childbirth reporting the highest levels of appreciation for life. In regression analyses, postpartum factors were significantly associated with overall growth and growth domains, taking into account other factors. The more the childbirth was perceived as central to the mothers' identity and the better the maternal attachment was to the child, the higher levels of growth. Growth was also negatively related to endorsement of childbirth PTSD. Background factors, such as maternal age, education, and prior mental health, were associated with specific growth domains, although the association was small and there was no association with overall growth. Post-birth factors are important in ensuing psychological growth in the first months following birth. Attention to opportunities of growth following childbirth is warranted in clinical care, in particular following traumatic childbirth.

Giving birth under hospital visitor restrictions: Heightened acute stress in childbirth in COVID-19 positive women.

As the novel coronavirus (COVID-19) has spread globally, a significant portion of women have undergone childbirth while possibly infected with the virus and also under social isolation due to hospital visitor restrictions. Emerging studies examined birth outcomes in COVID-19 positive women, but knowledge of the psychological experience of childbirth remains lacking. This study survey concerning childbirth and mental health launched during the first wave of the pandemic in the US. Women reporting confirmed/suspected COVID-19 during childbirth were matched on various background factors with women reporting COVID-19 negative. We found higher prevalence of clinically significant acute stress in birth in COVID-19 positive women. This group was 11 times as likely to have no visitors than matched controls and reported higher levels of pain in delivery, lower newborn weights, and more infant admission to neonatal intensive care units. Visitor restrictions were associated with these birth outcomes. COVID-19 positive women with no visitors were 6 times as likely to report clinical acute stress in birth than COVID-19 positive women with visitors. The findings underscore increased risk for childbirth-induced psychological morbidity in COVID-19-affected populations. As hospitals continue to revise policies concerning visitor restrictions, attention to the wellbeing of new mothers is warranted.

Giving birth under hospital visitor restrictions: Heightened acute stress in childbirth in COVID-19 positive women.

As the novel coronavirus (COVID-19) has spread globally, a significant portion of women have undergone childbirth while possibly infected with the virus and also under social isolation due to hospital visitor restrictions. Emerging studies examined birth outcomes in COVID-19 positive women, but knowledge of the psychological experience of childbirth remains lacking. This study survey concerning childbirth and mental health launched during the first wave of the pandemic in the US. Women reporting confirmed/suspected COVID-19 during childbirth were matched on various background factors with women reporting COVID-19 negative. We found higher prevalence of clinically significant acute stress in birth in COVID-19 positive women. This group was 11 times as likely to have no visitors than matched controls and reported higher levels of pain in delivery, lower newborn weights, and more infant admission to neonatal intensive care units. Visitor restrictions were associated with these birth outcomes. COVID-19 positive women with no visitors were 6 times as likely to report clinical acute stress in birth than COVID-19 positive women with visitors. The findings underscore increased risk for childbirth-induced psychological morbidity in COVID-19-affected populations. As hospitals continue to revise policies concerning visitor restrictions, attention to the wellbeing of new mothers is warranted.