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Related Concept Videos

Equity Theory01:26

Equity Theory

Equity theory explains how our sense of fairness influences the dynamics of close relationships. Rooted in social psychology, the theory posits that individuals evaluate fairness by comparing the ratio of their contributions to the rewards they receive. Relationship satisfaction is highest when these ratios are perceived as balanced between partners, promoting mutual reciprocity and a sense of justice.Equity vs. Equality in RelationshipsEquity is distinct from equality. Fairness does not...
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Data Validation

Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Data Validation01:03

Data Validation

Data validation is an essential part of a comprehensive assessment. Validation is confirming or verifying and opening the door to gathering more assessment data as it clarifies vague or unclear data. The process of checking and verifying the collected information is called data validation. The primary purpose of data validation is to ensure data is as free from error, bias, and misinterpretation as possible.
Nursing assessment guides are generally based on holistic models rather than medical...
Stratified Sampling Method01:16

Stratified Sampling Method

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Strategies for Assessing and Addressing Confounding

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.
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

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Related Experiment Video

Updated: Jul 17, 2026

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
10:39

Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning

Published on: August 29, 2025

Methods for Defining Equity-Stratifying Variables: A Systematic Review of Validation Studies.

Hana Osman1, Taylor Hecker1, Zarin Hossain1

  • 1Department of Medicine, University of Calgary, Calgary, Canada.

Journal of Clinical Epidemiology
|July 15, 2026
PubMed
Summary

This systematic review found varied accuracy in health data definitions for social disadvantage factors. More validation is needed to reliably measure health equity and social inequalities.

Keywords:
case definitionshealth equitysocial determinants of healthvalidation study

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Area of Science:

  • Epidemiology
  • Health Informatics
  • Social Determinants of Health

Background:

  • Disease burden disproportionately affects socially disadvantaged populations.
  • The PROGRESS-Plus framework categorizes key social determinants of health.
  • Accuracy of case definitions for these variables in health data is largely unknown.

Purpose of the Study:

  • Systematically review definitions and validation of PROGRESS-Plus variables in health data.
  • Assess the application of equity-stratifying variables in epidemiologic studies.
  • Evaluate data sources like administrative health, population-level, and EHR data.

Main Methods:

  • Searched Medline, EMBASE, CINAHL, Web of Science, and Google Scholar up to 2024.
  • Included validation studies of equity-stratifying variables in adults using administrative or EHR data.
  • Extracted and synthesized data sources, algorithms, and performance measures; meta-analysis was not feasible due to heterogeneity.

Main Results:

  • 116 studies were included from 9099 citations; most (55%) published 2019-2024.
  • Race/ethnicity definitions were most common (57%), using codes or surname algorithms.
  • High variability in performance measures (sensitivity, PPV, Cohen's Kappa) for race/ethnicity; lowest variation for occupation.

Conclusions:

  • Increasing publications validate equity-stratifying variables, but performance measures vary widely.
  • Significant heterogeneity in variable definitions and validation methods exists.
  • Further rigorous validation of equity-stratifying variables in health data is essential.