Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6699529 | PMC |
| http://dx.doi.org/10.4300/JGME-D-19-00464.1 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
They paid attention to the whole of me in some way, both physically, mentally, and everything in between: a qualitative study of patients' experiences of interdisciplinary rehabilitation (PREVSAM) in primary care for musculoskeletal disorders.
Scand J Prim Health Care
December 2024
Unit of Physiotherapy, Department of Health and Rehabilitation, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
Purpose: To explore and describe patients' experiences and perceptions of rehabilitation according to the rehabilitation model 'Prevention of sickness absence through early identification and rehabilitation of at-risk patients with musculoskeletal pain' (PREVSAM).
Method: A qualitative study was conducted, with individual semi-structured interviews analysed using qualitative content analysis. Fifteen patients from three primary care rehabilitation clinics in Sweden who had undergone rehabilitation based on the PREVSAM model participated.
Community health promotion and disaster recovery: a PhotoVoice project in Comerío, Puerto Rico.
Arts Health
December 2024
Department of Urban & Regional Planning, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL, USA.
Background: PhotoVoice - a series of workshops involving participatory photography and narrative-building - was employed in the rural town of Comerío, Puerto Rico (PR) to describe disaster recovery in a rural setting and foster policy dialogue.
Methods: Using PhotoVoice workshops and ethnographic observations, the project describes how women affiliated with a local community-based organization described the priorities for disaster recovery in visual images and narratives. We draw analytically upon theories of intersectionality and coloniality to describe socio-structural and community factors that shape community health in the context of ongoing disasters.
Highly Efficient Analysis on Biomass Carbohydrate Mixtures by DREAMTIME NMR Spectroscopy.
Anal Chem
December 2024
Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, State Key Laboratory of Physical Chemistry of Solid Surfaces, Xiamen University, Siming South Road 422, Xiamen 361005, China.
Proton (H) NMR spectroscopy presents a powerful tool for biomass mixture studies by revealing the involved chemical compounds with identified ingredients and molecular structures. However, conventional H NMR generally suffers from spectral congestion when measuring biomass mixtures, particularly biomass carbohydrate samples, that contain various physically and chemically similar compounds. In this study, a targeted detection NMR approach, DREAMTIME, is exploited for studying biomass carbohydrate mixtures by spectroscopically targeting the desired compounds in separate 1D NMR spectra.
View Article and Find Full Text PDFDevelopment of a set of indicators for the quality of chronic pain management in Chinese community-dwelling older adults: a Delphi study.
BMC Geriatr
December 2024
Harbin Medical University, Daqing, Heilongjiang Province, China.
Background: Standardized and systematic quality assessments of chronic pain management, particularly among older adult populations, are lacking in resource-limited community settings. A specific set of indicators to evaluate the quality of chronic pain management in this population has yet to be developed. Therefore, the present study constructed a set of indicators to assess the quality of chronic pain management in Chinese community-dwelling older adults, providing a standardized reference and guidance for community health centers to manage chronic pain in this population.
View Article and Find Full Text PDFApproaching coupled-cluster accuracy for molecular electronic structures with multi-task learning.
Nat Comput Sci
December 2024
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties.
View Article and Find Full Text PDFWant 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!