Objective: To estimate how social support and social conflict relate to prenatal depressive symptoms and to generate a brief clinical tool to identify women at increased psychosocial risk.
Methods: This is a prospective study following 1,047 pregnant women receiving care at two university-affiliated clinics from early pregnancy through 1 year postpartum. Structured interviews were conducted in the second trimester of pregnancy. Hierarchical and logistic regressions were used to examine potential direct and interactive effects of social support and conflict on prenatal depressive symptoms measured by the Center for Epidemiologic Studies-Depression Scale.
Results: Thirty-three percent of the sample reported elevated levels of depressive symptoms predicted from sociodemographic factors, social support, and social conflict. Social support and conflict had independent effects on depressive symptoms although social conflict was a stronger predictor. There was a "dose-response," with each increase in interpersonal risk factor resulting in consequent risk for probable depression based on symptom reports (Center for Epidemiologic Studies-Scale greater than or equal to 16). A composite of one social support and three conflict items were identified to be used by clinicians to identify interpersonal risk factors for depression in pregnancy. Seventy-six percent of women with a composite score of three or more high-risk responses reported depressive symptoms.
Conclusion: Increased assessment of social support and social conflict by clinicians during pregnancy can identify women who could benefit from group or individual interventions to enhance supportive and reduce negative social interactions.
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http://dx.doi.org/10.1097/01.AOG.0000265352.61822.1b | DOI Listing |
JMIR Aging
January 2025
Department of Computing, Faculty of Computer and Mathematical Sciences, Hong Kong Polytechnic University, Hung Hom, China (Hong Kong).
Background: Providing ongoing support to the increasing number of caregivers as their needs change in the long-term course of dementia is a severe challenge to any health care system. Conversational artificial intelligence (AI) operating 24/7 may help to tackle this problem.
Objective: This study describes the development of a generative AI chatbot-the PDC30 Chatbot-and evaluates its acceptability in a mixed methods study.
Purpose: After recent policy and practice changes, health care schools are expected to involve patients as partners in the management, design, and delivery of professional curricula. However, what these partnerships mean for academic communities and the processes needed to support them are not yet understood. This study examines what involving patients as partners within an academic community means for key stakeholders.
View Article and Find Full Text PDFPurpose: In response to the need to support health care professionals during the COVID-19 pandemic, an innovative, peer-led discussion group program for medical school faculty, called CIRCLE (Colleague Involved in Reaching Colleagues through Listening and Empathy), was developed at Rutgers Health. This article describes results of a qualitative analysis of the participants' experiences, explores virtual communication platform use during this peer support program, and identifies the program's beneficial elements.
Method: CIRCLE was inaugurated in October 2020 at Rutgers New Jersey Medical School and Rutgers Robert Wood Johnson Medical School using evidence-informed topics.
Harv Rev Psychiatry
December 2024
From McLean Hospital (Mr. Mermin and Dr. Choi-Kain) Belmont, MA; Harvard College (Ms. Steigerwald); Harvard Medical School (Dr. Choi-Kain).
Borderline personality disorder (BPD) has been described as a condition of intolerance of aloneness. This characteristic drives distinguishing criteria, such as frantic efforts to avoid abandonment. Both BPD and loneliness are linked with elevated mortality risk and multiple negative health outcomes.
View Article and Find Full Text PDFYouth living with HIV (YLWH) have high rates of virologic failure due to medication non-adherence. is a novel, gamified mobile health (mHealth) application designed with user-centered principles to improve medication adherence by integrating medication reminders with social and financial incentives, virtual peer social support and early clinic outreach for non-adherent YLWH in Nigeria. Focus Group Discussions (FGDs) were conducted to identify reactions to key prototype features (user interface, medication reminders, incentives, and peer support), facilitators and barriers to app use, and how well the app would meet adherence needs.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!