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A comparative study of domestic decision-making power and social support as predictors of postpartum depressive and physical symptoms between immigrant and native-born women.

ABSTRACT: BACKGROUND:Women's participation in decision-making in the household is an indicator of women's empowerment. Few studies have compared domestic decision-making power and its effect on postpartum health between immigrant and native-born women. This study aimed to examine the effect of domestic decision-making power and social support during pregnancy on predicting postpartum depressive and physical symptoms among immigrant and native-born mothers in Taiwan. METHODS AND FINDINGS:This prospective study recruited 177 marriage-based immigrant mothers and 230 native-born women who were at least twelve weeks pregnant from hospitals, clinics and health centers. Data were collected in the 2nd or 3rd trimester of pregnancy and at 3 months postpartum from March 2013 to March 2015. Postpartum depression and the severity of postpartum physical symptoms were measured using the Edinburgh Postnatal Depression Scale, and a 17-item, 4-point Likert scale, respectively. Linear regression was used to examine the relationship between "domestic decision-making power and social support during pregnancy" and "depressive and physical symptoms at 3 months postpartum." Women who had lower domestic decision-making power and social support during pregnancy had higher postpartum depressive and physical symptoms. Those women with full-time employment and insufficient family income had higher postpartum depressive symptoms. Though immigrant women scored lower in domestic decision-making power and social support than native-born women, they had lower mean scores in postpartum depressive and physical symptoms. After accounting for the abovementioned factors, immigrant women remained at lower risk for postpartum depressive and physical symptoms than native-born women. There was significant interaction between domestic decision-making power and immigrant status, suggesting that the association between domestic decision-making and postpartum depressive and physical symptoms was smaller for immigrants than for native women. CONCLUSIONS:Domestic decision-making power and social support during pregnancy are protective predictors of postpartum depressive and physical symptoms. However, the effect of domestic decision-making power appeared to be less salient for immigrants, probably due to the "healthy immigrant effect" and/or lower expectations toward domestic decision-making power among immigrants. The finding that immigrant women demonstrated a lower level of domestic decision-making power suggests that empowerment issues need to be addressed among immigrants.

SUBMITTER: Chen HH

PROVIDER: S-EPMC7141669 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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A comparative study of domestic decision-making power and social support as predictors of postpartum depressive and physical symptoms between immigrant and native-born women.

Chen Hung-Hui HH Chien Li-Yin LY

PloS one 20200408 4

<h4>Background</h4>Women's participation in decision-making in the household is an indicator of women's empowerment. Few studies have compared domestic decision-making power and its effect on postpartum health between immigrant and native-born women. This study aimed to examine the effect of domestic decision-making power and social support during pregnancy on predicting postpartum depressive and physical symptoms among immigrant and native-born mothers in Taiwan.<h4>Methods and findings</h4>Thi ...[more]

PMID: 32267897

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Project description:BackgroundThere is a critical need to improve support for families making difficult shared decisions about patient care with clinicians in the neuroscience ICU (neuro-ICU). The aim of this study is to identify patient- and family-related factors associated with dissatisfaction with shared decision-making support among families of neuro-critically ill patients.MethodsWe conducted a retrospective observational cohort study using survey data that had been collected from a consecutive sample of family members of patients in the neuro-ICU (one family member per patient) at two US academic centers. Satisfaction with shared decision-making support on ICU discharge had been measured among family members using one specific Likert scale item on the Family Satisfaction in the ICU 24 survey, a validated survey instrument for families of patients in the ICU. We dichotomized top-box responses for this particular item as an outcome variable and identified available patient- and family-related covariates associated with dissatisfaction (i.e., less than complete satisfaction) via univariate and multivariate analyses.ResultsAmong 355 surveys, 180 (49.5%) of the surveys indicated dissatisfaction with support during decision-making. In a multivariate model, no preexisting characteristics of families or patients ascertainable on ICU admission were predictive of dissatisfaction. However, among family factors determined during the ICU course, experiencing three or fewer formal family meetings (odds ratio 1.93 [confidence interval 1.13-3.31]; p = 0.01) was significantly predictive of dissatisfaction with decisional support in this cohort with an average patient length of stay of 8.6 days (SD 8.4). There was also a trend toward a family's decision to keep a patient as full code, without treatment limitations, being predictive of dissatisfaction (odds ratio 1.80 [confidence interval 0.93-3.51]; p = 0.08).ConclusionsFamily dissatisfaction with neuro-ICU shared decision-making support is not necessarily predicted by any preexisting family or patient variables but appears to correlate with participating in fewer formal family meetings during ICU admission. Future studies to improve family satisfaction with neurocritical care decision-making support should have broad inclusion criteria for participants and should consider promoting frequency of family meetings as a core strategy.

Project description:With the ever-increasing societal dependence on electricity, one of the critical tasks in power supply is maintaining the power line infrastructure. In the process of making informed, cost-effective, and timely decisions, maintenance engineers must rely on human-created, heterogeneous, structured, and also largely unstructured information. The maturing research on vision-based power line inspection driven by advancements in deep learning offers first possibilities to move towards more holistic, automated, and safe decision-making. However, (current) research focuses solely on the extraction of information rather than its implementation in decision-making processes. The paper addresses this shortcoming by designing, instantiating, and evaluating a holistic deep-learning-enabled image-based decision support system artifact for power line maintenance at a German distribution system operator in southern Germany. Following the design science research paradigm, two main components of the artifact are designed: A deep-learning-based model component responsible for automatic fault detection of power line parts as well as a user-oriented interface responsible for presenting the captured information in a way that enables more informed decisions. As a basis for both components, preliminary design requirements are derived from literature and the application field. Drawing on justificatory knowledge from deep learning as well as decision support systems, tentative design principles are derived. Based on these design principles, a prototype of the artifact is implemented that allows for rigorous evaluation of the design knowledge in multiple evaluation episodes, covering different angles. Through a technical experiment the technical novelty of the artifact’s capability to capture selected faults (regarding insulators and safety pins) in unmanned aerial vehicle (UAV)-captured image data (model component) is validated. Subsequent interviews, surveys, and workshops in a natural environment confirm the usefulness of the model as well as the user interface component. The evaluation provides evidence that (1) the image processing approach manages to address the gap of power line component inspection and (2) that the proposed holistic design knowledge for image-based decision support systems enables more informed decision-making. The paper therefore contributes to research and practice in three ways. First, the technical feasibility to detect certain maintenance-intensive parts of power lines with the help of unique UAV image data is shown. Second, the distribution system operators’ specific problem is solved by supporting decisions in maintenance with the proposed image-based decision support system. Third, precise design knowledge for image-based decision support systems is formulated that can inform future system designs of a similar nature. Supplementary Information The online version contains supplementary material available at 10.1007/s12599-022-00745-z.

Dataset Information

A comparative study of domestic decision-making power and social support as predictors of postpartum depressive and physical symptoms between immigrant and native-born women.

Publications

A comparative study of domestic decision-making power and social support as predictors of postpartum depressive and physical symptoms between immigrant and native-born women.

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