Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Data Reporting and Recording01:24

Data Reporting and Recording

5.1K
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...
5.1K
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

190
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
190
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

666
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
666
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.0K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.0K
Halo Effect01:27

Halo Effect

113
The halo effect is a cognitive bias in which an individual's overall impression influences judgments about their specific traits. This psychological phenomenon leads people to associate positive characteristics with those they perceive as generally good and negative characteristics with those they view as bad. This effect is particularly influential in social perception, professional evaluations, and decision-making processes.The Psychological Basis of the Halo EffectThe halo effect is rooted...
113
Types of Records II: Educational and Administrative Records01:18

Types of Records II: Educational and Administrative Records

868
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:
868

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Landscape Overview of Integrated Data Systems Funding and Staffing Models Across the U.S.

International journal of population data science·2026
Same author

Building the Iowa Data Drive: a participatory approach to developing early childhood indicators for state and local policymaking.

International journal of population data science·2025
Same author

Prevalence and Changes in Usage of Mental Health Services for Rhode Island Children and Youth Before, During, and After Onset of the COVID-19 Pandemic.

The Psychiatric quarterly·2024
Same author

Four questions to guide decision-making for data sharing and integration.

International journal of population data science·2024
Same author

Leveraging integrated data for program evaluation: Recommendations from the field.

Evaluation and program planning·2022
Same author

Developmental impacts of the COVID-19 pandemic on young children: a conceptual model for research with integrated administrative data systems.

International journal of population data science·2021

Related Experiment Video

Updated: Nov 5, 2025

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
14:43

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

12.3K

A framework for centering racial equity throughout the administrative data life cycle.

Amy L Hawn Nelson1, Sharon Zanti1

  • 1University of Pennsylvania.

International Journal of Population Data Science
|May 19, 2021
PubMed
Summary

Centering racial equity in government data integration requires acknowledging bias and implementing practical steps across the data lifecycle. This framework guides agencies toward more equitable data practices for community benefit.

Keywords:
Keywords racial equitydata integrationparticipatory action researchpublic deliberation

More Related Videos

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K

Related Experiment Videos

Last Updated: Nov 5, 2025

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
14:43

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting

Published on: January 12, 2018

12.3K
Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
06:55

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

Published on: January 8, 2020

14.8K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K

Area of Science:

  • Public Administration
  • Data Science
  • Racial Equity Studies

Background:

  • Government data integration aims for public good but often lacks racial equity focus.
  • Existing data infrastructure can perpetuate systemic racial inequities due to unaddressed structural bias.
  • Civic data users and the public are seldom involved in data system development.

Purpose of the Study:

  • To present a collaborative framework for centering racial equity in data integration.
  • To provide site-based examples of "Work in Action" demonstrating equitable data practices.
  • To develop a practical toolkit for agencies to address bias in data.

Main Methods:

  • Participatory action research and public deliberation were employed.
  • A diverse 15-person civic data stakeholder workgroup was convened.
  • A framework was co-created, detailing best practices across the data lifecycle.

Main Results:

  • A framework was developed, covering six stages of the administrative data lifecycle.
  • For each stage, positive and problematic practices for racial equity are outlined.
  • Site-based examples and a "Toolkit for Centering Racial Equity Throughout Data Integration" were produced.

Conclusions:

  • Centering racial equity in data integration is an ongoing process, not a binary state.
  • The framework offers concrete strategies for organizations to advance equitable data practices.
  • Numerous opportunities exist to embed racial equity throughout the data lifecycle.