Background: External supervision of primary health care facilities to monitor and improve services is common in low-income countries. Currently there are no tools to measure the quality of support in external supervision in these countries.
Aim: To develop a provider-reported instrument to assess the support delivered through external supervision in Rwanda and other countries.
Methods: "External supervision: Provider Evaluation of Supervisor Support" (ExPRESS) was developed in 18 steps, primarily in Rwanda. Content validity was optimised using systematic search for related instruments, interviews, translations, and relevance assessments by international supervision experts as well as local experts in Nigeria, Kenya, Uganda and Rwanda. Construct validity and reliability were examined in two separate field tests, the first using exploratory factor analysis and a test-retest design, the second for confirmatory factor analysis.
Results: We included 16 items in section A ('The most recent experience with an external supervisor'), and 13 items in section B ('The overall experience with external supervisors'). Item-content validity index was acceptable. In field test I, test-retest had acceptable kappa values and exploratory factor analysis suggested relevant factors in sections A and B used for model hypotheses. In field test II, models were tested by confirmatory factor analysis fitting a 4-factor model for section A, and a 3-factor model for section B.
Conclusions: ExPRESS is a promising tool for evaluation of the quality of support of primary health care providers in external supervision of primary health care facilities in resource-constrained settings. ExPRESS may be used as specific feedback to external supervisors to help identify and address gaps in the supervision they provide. Further studies should determine optimal interpretation of scores and the number of respondents needed per supervisor to obtain precise results, as well as test the functionality of section B.
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http://dx.doi.org/10.1080/16549716.2018.1445466 | DOI Listing |
Nurs Health Sci
March 2025
College of Nursing, Pusan National University, Yangsan, Republic of Korea.
This study aimed to externally validate two 10-year stroke risk prediction models: one developed in Korea (Model 1) and the other in China (Model 2), using community-based cohort data. Data from 8432 participants in Model 1 and 8915 participants in Model 2 were analyzed. Stroke risk was calculated based on each model's equations, and model performance was assessed using the area under the receiver operating characteristic curve (AUC).
View Article and Find Full Text PDFEnviron Health Prev Med
January 2025
Division of Environmental Medicine and Population Sciences, Department of Social and Environmental Medicine, Graduate School of Medicine, Osaka University.
Background: A comprehensive understanding of the epidemiology of pediatric out-of-hospital cardiac arrest (OHCA) occurring under school supervision is lacking. We aimed to comprehensively describe the characteristics and outcomes of OHCA among students in elementary schools, junior high schools, high schools, and technical colleges in Japan.
Methods: OHCA data from 2008-2021 were obtained from the SPIRITS study, which provides a nationwide database of OHCAs occurring under school supervision across Japan.
Ophthalmol Sci
November 2024
Liverpool Ocular Oncology Research Group, Department of Eye and Vision Science, Institute of Life Course and Medical Sciences (ILCaMS), University of Liverpool, Liverpool, United Kingdom.
Purpose: Testing the validity of a self-supervised deep learning (DL) model, RETFound, for use on posterior uveal (choroidal) melanoma (UM) and nevus differentiation.
Design: Case-control study.
Subjects: Ultrawidefield fundoscopy images, both color and autofluorescence, were used for this study, obtained from 4255 patients seen at the Liverpool Ocular Oncology Center between 1995 and 2020.
Food Chem
January 2025
University of Parma, Department of Chemistry, Life Sciences and Environmental Sustainability, Parco Area delle Scienze 17/A, 43124 Parma, Italy.
The complexity of modern food supply chains limits the effectiveness of targeted approaches to address food traceability issues. Untargeted metabolomics provides a comprehensive profile of small molecules present within biological samples. In this study, the potential of ultra-high performance liquid chromatography-ion mobility-high resolution mass spectrometry (UHPLC-IMS-HRMS) to discriminate bovine milk samples collected at individual level was evaluated for traceability purposes.
View Article and Find Full Text PDFJ Mol Biol
January 2025
School of Mathematics and Physics, University of Science and Technology Beijing, Beijing, 100083, China. Electronic address:
Single-cell RNA sequencing (scRNA-seq) analysis offers tremendous potential for addressing various biological questions, with one key application being the annotation of query datasets with unknown cell types using well-annotated external reference datasets. However, the performance of existing supervised or semi-supervised methods largely depends on the quality of source data. Furthermore, these methods often struggle with the batch effects arising from different platforms when handling multiple reference or query datasets, making precise annotation challenging.
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