Thesis (M.S.M.) PLEASE NOTE: Boston University Libraries did not receive an Authorization To Manage form for this thesis or dissertation. It is therefore not openly accessible, though it may be available by request. If you are the author or principal advisor of this work and would like to request open access for it, please contact us at open-help@bu.edu. Thank you. / OBJECTIVE: To examine the association between maternal trauma exposure and children's social skills in preschool. METHODS: We nested a prospective cohort study within an ongoing randomized controlled trial of a maternal depression prevention intervention. Each participating mother had a three to five year-old child in Head Start. Maternal trauma history was assessed at baseline. Six months later, Head Start teachers (masked to both study arm and mothers' depression status) completed the Social Skills Rating system (SSRS) to assess children's social skills and problem behaviors. SSRS scores of children of mothers with and without a trauma history were compared using t-tests for bivariate comparisons. To evaluate for potential effect measure modification and confounding, we conducted a stratified analysis by the variables of interest. We evaluated for effect measure modification by comparing stratum-specific estimates to each other. we then evaluated for confounding by comparing the standardized to the crude estimates. Finally, we conducted a multivariate analysis, adjusting for potential confounders. RESULTS: Eighty-two mother-child pairs completed the baseline and follow-up evaluations. Sixty mothers (73%) reported a history of trauma. The most common traumatic exposures included emotional abuse (58%), the violent death of a close contact (53%), and physical assault (43%). In the bivariate analysis, children of mothers with a history of trauma had lower overall social skills scores when compared to children of mothers without trauma [101.48 (54th percentile) vs. 109.18 (66th percentile), p. 0.04]. When adjusted for multiple potential confounders, mean social skills scores were 10.01 points lower (95% CI -18.88, -1.14) for children of mothers with a history of trauma. There was no evidence of effect measure modification by study group assignment, ethnicity single parenthood, or depression score. CONCLUSIONS: Among urban Head Start mothers, trauma exposure is common and is associated with lower child social skills. Because social skills are a critical aspect of kindergarten readiness, specifically addressing maternal trauma in preschool programs that serve high-risk populations may be important. / 2031-01-01

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The purpose of the current study was to investigate the dynamic process of disclosure within the adolescent–mother relationship by examining maternal characteristics that encourage adolescent disclosure of distressing experiences and risk factors that may interfere with mothers’ abilities to be supportive. A community sample of 66 mothers and their adolescent children (M = 14.31 years, 58% female) participated. The adolescents disclosed an emotionally distressing experience to their mothers for the first time. Mothers’ validating behaviors and emotional distress in response to their adolescents’ expressions of negative emotion were predictive of adolescent disclosure. Adolescents who perceived their mothers to be validating of their negative emotions made more substantive disclosures and found disclosing to their mothers to be more beneficial. In contrast, greater maternal emotional distress was associated with less substantive disclosures, and maternal emotional distress was further indirectly associated with less substantive and beneficial disclosures through less maternal validation of negative emotion. A developmental model of maternal risk for emotional distress in response to adolescent negative emotion was also supported. Maternal history of childhood trauma perpetrated by someone close to the mother (i.e., high betrayal) was associated with an increased likelihood of experiencing subsequent interpersonal trauma as an early adult; maternal interpersonal trauma in early adulthood was associated with mothers’ increased difficulty regulating their emotions; and greater maternal emotion dysregulation was associated with higher levels of maternal distress in response to adolescent negative emotion. An indirect association between maternal childhood high betrayal trauma and emotional distress was also supported through continued trauma and emotion regulation difficulties. These findings suggest that when disclosing distressing experiences to their mothers, adolescents consider how validating their mothers are of their expression of negative emotion, as well as how distressing their emotions are for their mothers. Mothers’ histories of childhood trauma, ongoing interpersonal trauma in adulthood, and emotion regulation difficulties were further implicated in mothers’ reactions to their adolescents’ expressions of negative emotion. Interventions targeted to increase maternal emotion regulation skills and validation of children’s negative emotions may be an effective way to promote better mother–adolescent communication, especially in regard to distressing experiences.

High-fidelity computer simulations of childbirth are time consuming, making them impractical for guiding decision-making during obstetric emergencies. The complex geometry, micro-structure, and large finite deformations undergone by the vagina during childbirth result in material and geometric nonlinearities, complicated boundary conditions, and nonhomogeneities within finite element (FE) simulations. Such nonlinearities pose a significant challenge for numerical solvers, increasing the computational time. Simplifying assumptions can reduce the computational time significantly, but this usually comes at the expense of simulation accuracy. The work herein proposed the use of reduced order modeling (ROM) techniques to create surrogate models that capture experimentally-measured displacement fields of rat vaginal tissue during inflation testing in order to attain both the accuracy of higher-fidelity models and the speed of lower-fidelity simulations. The proper orthogonal decomposition (POD) method was used to extract the significant information from FE simulations generated by varying the luminal pressure and the parameters that introduce the anisotropy in the selected constitutive model. In our first study, a new data-driven (DD) variational multiscale (VMS) ROM framework was extended to obtain the displacement fields of rat vaginal tissue subjected to ramping luminal pressure. For comparison purposes, we also investigated the classical Galerkin ROM (G-ROM). In our numerical study, both the G-ROM and the DD-VMS-ROM decreased the FE computational cost by orders of magnitude without a significant decrease in numerical accuracy. Furthermore, the DD-VMS-ROM improved the G-ROM accuracy at a modest computational overhead. Our numerical investigation showed that ROM had the potential to provide efficient and accurate computational tools to describe vaginal deformations, with the ultimate goal of improving maternal health. Our second study compared two common computational strategies for surrogate modeling, physics-based G-ROM and data-driven machine learning (ML), for decreasing the cost of FE simulations of the ex vivo deformations of rat vaginal tissue subjected to inflation testing to study the effect of a pre-imposed tear. Since there are many methods associated with each modeling approach, to provide a fair and natural comparison, we selected a basic model from each category. From the ROM strategies, we considered a simplified G-ROM that is based on the linearization of the underlying nonlinear FE equations. From the ML strategies, we selected a feed-forward dense neural network (DNN) to create mappings from constitutive model parameters and luminal pressure values to either the FE displacement history (in which case we denote the resulting model ML) or the POD coefficients of the displacement history (in which case we denote the resulting model POD-ML). The numerical comparisons of G-ROM, ML, and POD-ML took place in the reconstructive regime. The numerical results showed that the G-ROM outperformed the ML model in terms of offline central processing unit (CPU) time for model training, online CPU time required to generate approximations, and relative error with respect to the FE models. The POD-ML model improved on the speed performance of the ML, having online CPU times comparable to those of the G-ROM given the same size of POD bases. However, the POD-ML model did not improve on the error performance of the ML. In our last study, we expanded our investigation of ML methods for surrogate modeling by comparing the performance of a DNN similar to what was used previously to that of a convolutional neural network (CNN) using 1-D convolution on the input parameters from FE simulations of active vaginal tearing. The new FE simulations utilized a custom continuum damage model that provided material damage and failure properties to an existing anisotropic hyperelastic constitutive model to replicate experimentally-observed tear propagation behaviors. We employed our DNN and CNN models to create mappings from constitutive model parameters, geometric properties of the propagating tear, and luminal pressure values to either the full FE displacement history or the POD coefficients of the displacement history. The root-mean-square error (RMSE) with respect to the FE displacement history achieved by full order output ML predictions was reproducible with POD-ML using a basis of only dimension l=10. Additionally, an order of magnitude reduction in offline time was observed using POD-ML over full-order ML with minimal difference between DNN and CNN architectures. Differences in online computational costs between ML and POD-ML were found to be negligible, but the DNNs produced predictions slightly faster than the CNNs, though both online times were on the same order of magnitude. While convolution did not significantly aid the regression task at hand, POD-ML was demonstrated to be an efficient and effective approach for surrogate modeling of the FE tear propagation model, approximating the displacement history with RMSE less than 0.1 mm and generating results 7 orders of magnitude faster than the FE model. This set of baseline numerical investigations serves as a starting point for future computer simulations that consider state-of-the-art G-ROM and ML strategies, and the in vivo geometry, boundary conditions, material properties, and tissue damage mechanics of the human vagina, as well as their changes during labor. / Doctor of Philosophy / Computer simulations of childbirth are extremely time-consuming, making them impractical for guiding decision-making by obstetricians when a patient is entering labor. The complex geometry, material microstructure, and large deformations undergone by the vagina during childbirth result in material and geometric properties that are challenging to mathematically model. Consequently, numerical solver methods (e.g., finite elements) require large amounts of time to simulate childbirth. Simplifying assumptions can reduce computational time, but this simplification usually comes at the expense of simulation accuracy. The work of this dissertation proposes the use of several techniques to reduce model complexity and create accurate approximations and predictions of results from full-order models (FOMs) with profound reductions in computational time. Our first study used reduced order models (ROMs) to extract the significant information from a FOM of the rat vagina subjected to inflation. We compared a basic ROM and an advanced, data-driven ROM. Our second study compared the basic ROM to a basic machine learning (ML) technique for approximating a FOM that simulated inflation of the rat vagina with a pre-imposed tear. A hybrid technique incorporating elements of both ROM and ML to approximate FOM results was also considered. Our final study made use of ML and hybrid techniques using a more advanced neural network (a convolutional neural network). These ML models were used to predict the results of a FOM simulation of vaginal tear propagation. These numerical investigations serve as a starting point for future development of computer simulations using state-of-the-art ROM and ML strategies as well as more realistic models for the mechanics of the human vagina during childbirth.