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American Journal of Public Health Aug 2017
Topics: Data Collection; Humans; Sexual and Gender Minorities; Social Stigma; United States; United States Dept. of Health and Human Services
PubMed: 28657780
DOI: 10.2105/AJPH.2017.303927 -
The Lancet. Planetary Health Nov 2020
Topics: Algorithms; Data Collection; Environment; Environmental Health; Humans; Public Health; Social Discrimination; Social Justice; Socioeconomic Factors
PubMed: 33159877
DOI: 10.1016/S2542-5196(20)30254-0 -
Spinal Cord Series and Cases 2019Cross-sectional and prospective cohort-study.
STUDY DESIGN
Cross-sectional and prospective cohort-study.
OBJECTIVES
To describe methodological issues, experienced challenges related to data collection in North Macedonia and to discuss possible improvements of epidemiological data collection in future studies.
SETTING
Clinic for Traumatology, Orthopedics, Anesthesia, Reanimation, Intensive Care Unit and Emergency Center, Mother Teresa Skopje University Hospital, Skopje and community settings, North Macedonia.
METHOD
A description of methodological challenges experienced in collecting data from 78 persons with acute and chronic traumatic spinal cord injury (SCI) examined and interviewed in 2015-2017 using a semiquantitative questionnaire and standard assessments tools.
RESULTS
This study identified three major challenges with data collection in this setting: (1) research logistics and procedures, such as recruitment, infrastructure, and compensation, (2) ethical issues and the initial lack of mutual trust and understanding between researchers and participants, and (3) scientific quality and interpretation, including representativeness.
CONCLUSIONS
Methodological issues influenced by settings, are important to consider when interpreting study results. Healthcare systems vary between (and sometimes in) countries, language and culture may introduce barriers to understanding, and epidemiological research also rely on infrastructure and surroundings. For this study, making time for and listening to the participants without being intruding was of special importance in building trust and a good relationship with the participants during recruiting participants and collecting data. We here provide suggestions regarding how to facilitate future epidemiological data collections in North Macedonia.
Topics: Cohort Studies; Cross-Sectional Studies; Data Collection; Humans; Prospective Studies; Republic of North Macedonia; Spinal Cord Injuries
PubMed: 31632716
DOI: 10.1038/s41394-019-0204-x -
Current Environmental Health Reports Dec 2019Neighborhood disorder has received attention as a determinant of health in urban contexts, through pathways that include psychosocial stress, perceived safety, and... (Review)
Review
PURPOSE OF REVIEW
Neighborhood disorder has received attention as a determinant of health in urban contexts, through pathways that include psychosocial stress, perceived safety, and physical activity. This review provides a summary of data collection methods, descriptive terms, and specific items employed to assess neighborhood disorder/order.
RECENT FINDINGS
The proliferation of methods and terminology employed in measuring neighborhood disorder (or neighborhood order) noted over the past two decades has made related studies increasingly difficult to compare. Following a search of peer-reviewed articles published from January 1998 to May 2018, this rapid literature review identified 18 studies that described neighborhood environments, yielding 23 broad terms related to neighborhood disorder/order, and a total of 74 distinct measurable items. A majority of neighborhood disorder/order measurements were assessed using primary data collection, often relying on resident self-report or investigatory observations conducted in person or using stored images for virtual audits. Items were balanced across signs of order or disorder, and further classification was proposed based on whether items were physically observable and relatively stable over time.
Topics: Data Collection; Environmental Health; Humans; Residence Characteristics
PubMed: 31773497
DOI: 10.1007/s40572-019-00259-z -
Bulletin of the World Health... Mar 2015Over the last decade, a massive increase in data collection and analysis has occurred in many fields. In the health sector, however, there has been relatively little... (Review)
Review
Over the last decade, a massive increase in data collection and analysis has occurred in many fields. In the health sector, however, there has been relatively little progress in data analysis and application despite a rapid rise in data production. Given adequate governance, improvements in the quality, quantity, storage and analysis of health data could lead to substantial improvements in many health outcomes. In low- and middle-income countries in particular, the creation of an information feedback mechanism can move health-care delivery towards results-based practice and improve the effective use of scarce resources. We review the evolving definition of big data and the possible advantages of - and problems in - using such data to improve health-care delivery in low- and middle-income countries. The collection of big data as mobile-phone based services improve may mean that development phases required elsewhere can be skipped. However, poor infrastructure may prevent interoperability and the safe use of patient data. An appropriate governance framework must be developed and enforced to protect individuals and ensure that health-care delivery is tailored to the characteristics and values of the target communities.
Topics: Data Collection; Delivery of Health Care; Developing Countries; Global Health; Humans; Income; Medical Informatics
PubMed: 25767300
DOI: 10.2471/BLT.14.139022 -
Public Health Genomics 2019Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of... (Review)
Review
BACKGROUND
Biomedical research has recently moved through three stages in digital healthcare: (1) data collection; (2) data sharing; and (3) data analytics. With the explosion of stored health data (HD), dental medicine is edging into its fourth stage of digitization using artificial intelligence (AI). This narrative literature review outlines the challenge of managing HD and anticipating the potential of AI in oral healthcare and dental research by summarizing the current literature.
SUMMARY
The basis of successful management of HD is the establishment of a generally accepted data standard that will guide its implementation within electronic health records (EHR) and health information technology ecosystems (HIT Eco). Thereby continuously adapted (self-) learning health systems (LHS) can be created. The HIT Eco of the future will combine (i) the front-end utilization of HD in clinical decision-making by providers using supportive diagnostic tools for patient-centered treatment planning, and (ii) back-end algorithms analyzing the standardized collected data to inform population-based policy decisions about resource allocations and research directions. Cryptographic methods in blockchain enable a safe, more efficient, and effective dental care within a global perspective. Key Message: The interoperability of HD with accessible digital health technologies is the key to deliver value-based dental care and exploit the tremendous potential of AI.
Topics: Artificial Intelligence; Data Collection; Dental Research; Humans; Medical Informatics Applications; Public Health
PubMed: 31390644
DOI: 10.1159/000501643 -
Social Studies of Science Jun 2024Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and...
Data are versatile objects that can travel across contexts. While data's travels have been widely discussed, little attention has been paid to the sites from where and to which data flow. Drawing upon ethnographic fieldwork in two connected data-intensive laboratories and the concept of domestication, we explore what it takes to bring data 'home' into the laboratory. As data come and dwell in the home, they are made to follow rituals, and as a result, data are reshaped and form ties with the laboratory and its practitioners. We identify four main ways of domesticating data. First, through about the data's origins, data practitioners draw the boundaries of their laboratory. Second, through , staff transform samples into digital data that can travel well while ruling what data can be let into the home. Third, through , data practitioners become familiar with their data and at the same time imprint the data, thus making them belong to their home. Finally, through , staff turn data into a resource for knowledge production. Through the lens of domestication, we see the data economy as a collection of homes connected by flows, and it is because data are tamed and attached to homes that they become valuable knowledge tools. Such domestication practices also have broad implications for staff, who in the process of 'homing' data, come to belong to the laboratory. To conclude, we reflect on what these domestication processes-which silence unusual behaviours in the data-mean for the knowledge produced in data-intensive research.
Topics: Anthropology, Cultural; Data Collection
PubMed: 38006306
DOI: 10.1177/03063127231212506 -
International Journal of Sports... Apr 2017Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion... (Review)
Review
Athlete preparation and performance continue to increase in complexity and costs. Modern coaches are shifting from reliance on personal memory, experience, and opinion to evidence from collected training-load data. Training-load monitoring may hold vital information for developing systems of monitoring that follow the training process with such precision that both performance prediction and day-to-day management of training become adjuncts to preparation and performance. Time-series data collection and analyses in sport are still in their infancy, with considerable efforts being applied in "big data" analytics, models of the appropriate variables to monitor, and methods for doing so. Training monitoring has already garnered important applications but lacks a theoretical framework from which to develop further. As such, we propose a framework involving the following: analyses of individuals, trend analyses, rules-based analysis, and statistical process control.
Topics: Athletes; Athletic Performance; Data Collection; Humans; Physical Conditioning, Human
PubMed: 27918664
DOI: 10.1123/ijspp.2016-0405 -
PloS One 2021Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to...
Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements.
Topics: Archives; Bibliometrics; Data Collection; Data Management; Humans; Information Dissemination; Psychology; Software; Surveys and Questionnaires
PubMed: 34019600
DOI: 10.1371/journal.pone.0252047 -
Yearbook of Medical Informatics Aug 2021To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations... (Review)
Review
OBJECTIVES
To identify gaps and challenges in health informatics and health information management during the COVID-19 pandemic. To describe solutions and offer recommendations that can address the identified gaps and challenges.
METHODS
A literature review of relevant peer-reviewed and grey literature published from January 2020 to December 2020 was conducted to inform the paper.
RESULTS
The literature revealed several themes regarding health information management and health informatics challenges and gaps: information systems and information technology infrastructure; data collection, quality, and standardization; and information governance and use. These challenges and gaps were often driven by public policy and funding constraints.
CONCLUSIONS
COVID-19 exposed complexities related to responding to a world-wide, fast moving, quickly spreading novel virus. Longstanding gaps and ongoing challenges in the local, national, and global health and public health information systems and data infrastructure must be addressed before we are faced with another global pandemic.
Topics: COVID-19; Data Accuracy; Data Collection; Humans; Information Management; Medical Informatics; Public Health Administration; Public Health Practice; United States
PubMed: 34479380
DOI: 10.1055/s-0041-1726499