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Related Concept Videos

Archival Research01:40

Archival Research

16.0K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
16.0K
Levels of Use of a GIS01:29

Levels of Use of a GIS

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Types of Records II: Educational and Administrative Records01:18

Types of Records II: Educational and Administrative Records

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Maintaining nurses' educational and administrative records in healthcare settings, including hospitals and nursing schools, is paramount. Here's a breakdown of the types of academic records mentioned:
788
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

49
Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
49
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.6K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.6K
Data Reporting and Recording01:24

Data Reporting and Recording

4.7K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
4.7K

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A Pathway Association Study Tool for GWAS Analyses of Metabolic Pathway Information
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A guide to navigating administrative data linkage for research.

Gursharan K Singh1,2, Alison P Bowers1,2

  • 1Centre for Healthcare Transformation, Faculty of Health, Queensland University of Technology (QUT), 60 Musk Avenue, Brisbane, QLD 4059, Australia.

European Journal of Cardiovascular Nursing
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Data linkage combines diverse datasets for richer health insights. Understanding costs, complexity, and storage is crucial for efficient data integration.

Keywords:
Administrative dataData linkageHeart failure

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Area of Science:

  • Health Informatics
  • Data Science
  • Public Health Research

Background:

  • Data linkage integrates information from administrative and research sources.
  • This process creates comprehensive datasets for health outcome analysis.
  • It aids in understanding pathways to improved public health.

Purpose of the Study:

  • To explore practical aspects of data linkage.
  • To identify key considerations for efficient data integration.
  • To highlight factors influencing the success of data linkage projects.

Main Methods:

  • Review of processes and practicalities in data linkage.
  • Analysis of factors such as cost, complexity, and storage.
  • Consideration of required applications and time lags.

Main Results:

  • Data linkage offers significant potential for health research.
  • Practical considerations like cost, complexity, and storage are critical.
  • Addressing these factors enhances the efficiency of data linkage.

Conclusions:

  • Efficient data linkage requires careful planning of practical aspects.
  • Understanding costs, complexity, and data management is key.
  • Optimizing these elements leads to more effective data integration for health insights.