Introduction: Developing a comprehensive cohort of people living with HIV (PLHIV) to help improve healthcare has long been the vision of researchers, clinicians and decision makers. The development of this kind of database is challenging and requires strict adherence to privacy and confidentiality policies. We explored procedures, activities and events in database development.
Objectives: To understand processes of developing a database with sensitive health information in Newfoundland and Labrador (NL), and to investigate procedures and activities to develop the database within its environmental context.
Methods: A narrative case study was used to explain the challenges and procedures involved in developing a database for our population. The development of the PLHIV cohort in NL is provided as an example to demonstrate the complexity of the process. We linked three datasets that included patient-level data for PLHIV: 1. laboratory data; 2. HIV clinic data; 3. health administrative data, which allowed for the creation of a large database containing many variables describing the PLHIV cohort in the province.
Results: We developed a de-identified cohort of 251 PLHIV that contained 178 variables. Our case study showed database development is an iterative process. The main challenges were ensuring patient privacy and data confidentiality are not compromised and working with multi-custodian data. These challenges were addressed by establishing a data governance team.
Conclusions: It is important that policy be implemented to merge siloed data sources in order to provide researchers with accurate and complete data that is required to conduct sound and precise research with maximum benefits for treatment and policy-making to improve health outcomes.
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http://dx.doi.org/10.23889/ijpds.v5i1.1144 | DOI Listing |
BJOG
January 2025
Department of Gynecology, Shanghai Key Laboratory of Maternal Fetal Medicine, Shanghai Institute of Maternal-Fetal Medicine and Gynecologic Oncology, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China.
Nurs Inq
January 2025
Medical Surgical Nursing Department, Faculty of Nursing, Alexandria University, Alexandria, Egypt.
Toxic workplace environments, especially those involving gaslighting, are known to contribute to stress and excessive work habits, such as workaholism, which may hinder a nurse's agility-an essential skill in adapting to fast-paced healthcare environments. However, the interplay between workplace gaslighting, workaholism, and agility in nursing remains underexplored. This study aims to investigate the relationship between workplace gaslighting, workaholism, and agility among nurses, focusing on how gaslighting moderates this relationship.
View Article and Find Full Text PDFJMIR Form Res
January 2025
School of Nursing, Li Ka Shing Faculty of Medicine, University of Hong Kong, 5/F, Academic Building, Pokfulam, Hong Kong, China (Hong Kong), 852 39176690.
Background: Breastfeeding is vital for the health and well-being of both mothers and infants, and it is crucial to create supportive environments that promote and maintain breastfeeding practices.
Objective: The objective of this paper was to describe the development of a breastfeeding-friendly app called "bfGPS" (HKU TALIC), which provides comprehensive territory-wide information on breastfeeding facilities in Hong Kong, with the goal of fostering a breastfeeding-friendly community.
Methods: The development of bfGPS can be categorized into three phases, which are (1) planning, prototype development, and preimplementation evaluation; (2) implementation and updates; and (3) usability evaluation.
Anal Methods
January 2025
School of Future Technology, Fujian Agriculture and Forestry University, Fuzhou 350002, China.
Near-infrared (NIR) spectroscopy, with its advantages of non-destructive analysis, simple operation, and fast detection speed, has been widely applied in various fields. However, the effectiveness of current spectral analysis techniques still relies on complex preprocessing and feature selection of spectral data. While data-driven deep learning can automatically extract features from raw spectral data, it typically requires large amounts of labeled data for training, limiting its application in spectral analysis.
View Article and Find Full Text PDFJ Eval Clin Pract
February 2025
Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China.
Aim(s): This study aims to evaluate the workload of clinical nurses by measuring the work relative value (work RVU) of common nursing items based on the resource-based relative value scale in China.
Background: Various single measurements have been employed to measure the nursing workload, but no comprehensive method has yet to be developed in China.
Methods: A descriptive study was conducted to construct a common item set for nursing work in general wards on the basis of the 2019 History Information System nursing database from Class A tertiary hospitals to identify the time associated with each service.
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