Background: Upwards of one in seven individuals experience perinatal depression and many individuals cannot access treatment. In response, perinatal depression is increasingly being managed in the obstetric setting. This study aimed to characterize the experiences of clinicians and clinician assistants to inform the extent to which clinician assistants can help address depression in obstetric settings.

Methods: This cross-sectional analysis used data from an ongoing cluster randomized control trial: The PRogram In Support of Moms (PRISM). Participants included clinicians (physicians, certified nurse midwives, nurse practitioners) and clinician assistants (medical assistants, nursing assistants). Baseline data regarding practices and attitudes of clinicians and clinician assistants toward addressing depression in the obstetric setting were described. Logistic regressions were used to examine the association of clinician time to complete work and depression management.

Results: Clinician assistants experienced significantly fewer time constraints than did clinicians. However, having adequate time to complete work was not significantly associated with increased depression management in clinicians. Clinician assistants reported feeling that addressing depression is an important part of their job, despite variation in doing so.

Conclusion: Clinician assistants are interacting with perinatal women extensively and are a vital part of obstetric care workflows. Clinician assistants report that they want to address depression and have time to do so. Thus, clinician assistants may be poised to help address the mental health needs of perinatal individuals.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10849797PMC
http://dx.doi.org/10.1080/0167482X.2021.1975676DOI Listing

Publication Analysis

Top Keywords

clinician assistants
40
assistants
12
perinatal depression
12
clinicians clinician
12
clinician
10
depression
9
assistants addressing
8
obstetric setting
8
help address
8
address depression
8

Similar Publications

The Impact of Physical Therapy Postprofessional Education Programs on Productivity in a Large Academic Medical Center.

J Phys Ther Educ

January 2025

John J. DeWitt is the associate director, education and professional development and associate clinical professor in the Rehab Services at The Ohio State University Wexner Medical Center, and School of Health & Rehabilitation Sciences, College of Medicine, The Ohio State University, 453 W 10th Ave, Rm 516, Columbus, OH 43210 Please address all correspondence to John J. DeWitt.

Introduction: Emerging evidence shows positive impact of postprofessional physical therapy education (residency and fellowship) specific to participants; however, outcomes on organizational impact are largely unknown. The purpose of this project was to describe the impact residency and fellowship training has on financial metrics. A secondary purpose of this case study was to describe trends associated with higher productivity.

View Article and Find Full Text PDF

Reimagining Physician Assistant Education: Championing Cognitive Diversity to Promote Inclusivity, Neurodiversity Awareness, and a Sense of Belonging.

J Physician Assist Educ

January 2025

Tonya C. George, PhD, MSHS, MSPH, PA-C, DFAAP, is a assistant professor, Doctor of Medical Science Program, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Philadelphia.

Neurodiversity, encompassing conditions such as autism spectrum disorder, attention-deficit/hyperactivity disorder, and dyslexia, represents a significant and often under-recognized segment of the population, including within science, technology, engineering, mathematics, and medicine fields like medicine. Neurodiverse individuals possess unique skills, including enhanced creativity, analytical thinking, and meticulous attention to detail, which are valuable in health care professions. However, failure to recognize and support these individuals can result in missed opportunities, social isolation, and mental health challenges.

View Article and Find Full Text PDF

Artificial intelligence-enhanced magnetic resonance imaging-based pre-operative staging in patients with endometrial cancer.

Int J Gynecol Cancer

January 2025

Institute of Image-Guided Surgery, IHU Strasbourg, France; University of Strasbourg, ICube, Laboratory of Engineering, Computer Science and Imaging, Department of Robotics, Imaging, Teledetection and Healthcare Technologies, CNRS, UMR, Strasbourg, France.

Objective: Evaluation of prognostic factors is crucial in patients with endometrial cancer for optimal treatment planning and prognosis assessment. This study proposes a deep learning pipeline for tumor and uterus segmentation from magnetic resonance imaging (MRI) images to predict deep myometrial invasion and cervical stroma invasion and thus assist clinicians in pre-operative workups.

Methods: Two experts consensually reviewed the MRIs and assessed myometrial invasion and cervical stromal invasion as per the International Federation of Gynecology and Obstetrics staging classification, to compare the diagnostic performance of the model with the radiologic consensus.

View Article and Find Full Text PDF

Application of ultrasound elastography and splenic size in predicting post-hepatectomy liver failure: Unveiling new clinical perspectives.

World J Gastroenterol

January 2025

Department of Gastroenterology, Nanchong Central Hospital, The Second Clinical Medical College, North Sichuan Medical College, Nanchong 637000, Sichuan Province, China.

In this article, we discuss the study by Cheng , published in the , focusing on predictive methods for post-hepatectomy liver failure (PHLF). PHLF is a common and serious complication, and accurate prediction is critical for clinical management. The study examines the potential of ultrasound elastography and splenic size in predicting PHLF.

View Article and Find Full Text PDF

Background: With the rising diagnostic rate of gallbladder polypoid lesions (GPLs), differentiating benign cholesterol polyps from gallbladder adenomas with a higher preoperative malignancy risk is crucial. This study aimed to establish a preoperative prediction model capable of accurately distinguishing between gallbladder adenomas and cholesterol polyps using machine learning algorithms.

Materials And Methods: We retrospectively analysed the patients' clinical baseline data, serological indicators, and ultrasound imaging data.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!