Objective: We investigated the support of self-management by health care providers (HCP) in prenatal Shared Medical Appointments (SMA).

Methods: on an topic list, semi-structured interviews were conducted. HCP who provided prenatal care in SMA in the last five years were recruited. Thematic analysis was used.

Results: We conducted 15 interviews. Four research themes were defined: didactic techniques, peer learning, motivation and the health care providers. Self-management support in SMA is based on peer-learning and is influenced by group dynamics. HCP play a role in the creation of an effective learning climate by using practical and communication techniques. HCP motivate participants for self-management through peer learning and person centered care. HCP need certain personality traits and leadership skills.

Conclusion: Self-management support in SMA is based on peer-learning and is influenced by group dynamics. HCP create an effective learning climate using practical and communication techniques and motivate participants for self-management through peer learning and person-centered care.

Innovation: This is the first study that gives insight in self-management support in SMA. HCP and medical schools should be aware of the fact that HCP in SMA need insight in didactic techniques, peer learning, group dynamics and leadership skills.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11399736PMC
http://dx.doi.org/10.1016/j.pecinn.2024.100337DOI Listing

Publication Analysis

Top Keywords

peer learning
20
self-management support
16
group dynamics
16
health care
12
care providers
12
didactic techniques
12
techniques peer
12
support sma
12
shared medical
8
medical appointments
8

Similar Publications

NNFit: A Self-Supervised Deep Learning Method for Accelerated Quantification of High- Resolution Short Echo Time MR Spectroscopy Datasets.

Radiol Artif Intell

January 2025

From the Department of Radiation Oncology (A.S.G., V.H., H.S.) and Department of Radiology and Imaging Sciences (B.D.W.), Emory University School of Medicine, 1701 Uppergate Dr, C5008 Winship Cancer Institute, Atlanta, GA 30322; Department of Radiology, University of Miami {School of Medicine?}, Miami, Fla (S.S., A.A.M.); Department of {Radiology?} Northwestern University {Feinberg School of Medicine?}, Chicago, Ill (L.A.D.C.); Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, Ga (Y.L.); Department of Psychology, Emory University, Atlanta, Ga (M.T.); and Department of Radiology, Duke University Medical Center, Durham, NC (B.J.S.).

Purpose To develop and evaluate the performance of NNFit, a self-supervised deep-learning method for quantification of high-resolution short echo-time (TE) echo-planar spectroscopic imaging (EPSI) datasets, with the goal of addressing the computational bottleneck of conventional spectral quantification methods in the clinical workflow. Materials and Methods This retrospective study included 89 short-TE whole-brain EPSI/GRAPPA scans from clinical trials for glioblastoma (Trial 1, May 2014-October 2018) and major-depressive-disorder (Trial 2, 2022- 2023). The training dataset included 685k spectra from 20 participants (60 scans) in Trial 1.

View Article and Find Full Text PDF

Posttraining Network Compression for 3D Medical Image Segmentation: Reducing Computational Efforts via Tucker Decomposition.

Radiol Artif Intell

January 2025

From the Department of Radiology, University Hospital, LMU Munich, Marchioninistr 15,81377 Munich, Germany (T.W., J.D., M.I.); Department of Statistics, LMU Munich, Munich, Germany (T.W., D.R.); and Munich Center for Machine Learning, Munich, Germany (T.W., J.D., D.R., M.I.).

Purpose To investigate whether the computational effort of 3D CT-based multiorgan segmentation with TotalSegmentator can be reduced via Tucker decomposition-based network compression. Materials and Methods In this retrospective study, Tucker decomposition was applied to the convolutional kernels of the TotalSegmentator model, an nnU-Net model trained on a comprehensive CT dataset for automatic segmentation of 117 anatomic structures. The proposed approach reduced the floating-point operations (FLOPs) and memory required during inference, offering an adjustable trade-off between computational efficiency and segmentation quality.

View Article and Find Full Text PDF

A Serial MRI-based Deep Learning Model to Predict Survival in Patients with Locoregionally Advanced Nasopharyngeal Carcinoma.

Radiol Artif Intell

January 2025

From the Department of Radiation Oncology, State Key Laboratory of Oncology in South China, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, 651 Dongfeng Road East, Guangzhou 510060, P. R. China (J.K., C.F.W., Z.H.C., G.Q.Z., Y.Q.W., L.L., Y.S.); Department of Radiation Therapy, Nanhai People's Hospital, The Sixth Affiliated Hospital, South China University of Technology, Foshan, China (J.Y.P., L.J.L.); and Department of Electronic Engineering, Information School, Yunnan University, Kunming, China (W.B.L.).

Purpose To develop and evaluate a deep learning-based prognostic model for predicting survival in locoregionally- advanced nasopharyngeal carcinoma (LA-NPC) using serial MRI before and after induction chemotherapy (IC). Materials and Methods This multicenter retrospective study included 1039 LA-NPC patients (779 male, 260 female, mean age 44 [standard deviation: 11]) diagnosed between April 2009 and December 2015. A radiomics- clinical prognostic model (Model RC) was developed using pre-and post-IC MRI and other clinical factors using graph convolutional neural networks (GCN).

View Article and Find Full Text PDF

Social learning plays an essential role in all cultural processes, but the factors underlying its evolution remain poorly understood. To understand how socio-ecological conditions affect social learning, we compared peering behavior (i.e.

View Article and Find Full Text PDF

Background: UK local authorities are developing and implementing Whole Systems Approaches to childhood obesity to tackle persistent and complex health inequalities. However, there is a lack of research on the practical application of these approaches. This paper reports on findings of a study into the initial implementation of this approach in Dundee, Scotland.

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!