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Related Concept Videos

Study Designs in Epidemiology01:20

Study Designs in Epidemiology

Epidemiological study designs are fundamental tools for investigating the distribution, determinants, and control of health conditions in populations. They help researchers understand the relationships between exposures and outcomes, and they broadly fall into two categories: "observational" and "experimental" studies.
Observational studies are those where the researcher does not intervene but rather observes natural variations. They include cross-sectional, cohort, and case-control studies.
Models of Health Promotion and Illness Prevention II01:18

Models of Health Promotion and Illness Prevention II

The person's health status fluctuates continually, varying from being in good health to becoming ill and returning to being healthy. To understand the concept of illness prevention, there are two models. First, the health-illness continuum model is a graphic representation of an individual's wellness. It states that a person is considered healthy in the absence of physical disease and the presence of good emotional health.
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Models of Health Promotion and Illness Prevention I01:25

Models of Health Promotion and Illness Prevention I

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Levels of Health Promotion and Illness Prevention01:26

Levels of Health Promotion and Illness Prevention

Health promotion allows a person to control the determinants of health, resulting in an improved health status. It enhances the quality of life and reduces premature deaths. Health promotion and illness prevention programs help people make beneficial choices to reduce the risk of disease and disabilities. There are three health promotion and illness prevention levels: primary, secondary, and tertiary prevention.
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Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

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A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
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A study protocol for applying the co-creating knowledge translation framework to a population health study.

Kathryn Powell1, Alison Kitson, Elizabeth Hoon

  • 1School of Population Health, The University of Adelaide, Adelaide 5005, Australia. kathryn.powell@adelaide.edu.au.

Implementation Science : IS
|August 30, 2013
PubMed
Summary

This study introduces the co-Knowledge Translation (co-KT) Framework, integrating population health research with community collaboration throughout the research process for effective health interventions.

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Area of Science:

  • Public Health Research
  • Knowledge Translation
  • Community-Based Participatory Research

Background:

  • Population health research and Knowledge Translation (KT) are often separate, limiting research impact.
  • A Port Lincoln, South Australia study utilized the co-KT Framework to bridge this gap.
  • The co-KT Framework facilitates collaborative knowledge creation between researchers and communities.

Purpose of the Study:

  • To develop and apply a novel framework (co-KT) for integrating Knowledge Translation into population health research.
  • To foster collaborative knowledge formation and intervention development with community partners.
  • To maximize the impact of research outcomes for community health improvement.

Main Methods:

  • Employed a five-step engaged scholarship and action research framework (co-KT Framework).
  • Iterative knowledge co-creation with the study population, incorporating context-specific details and knowledge exchange.
  • Systematic data collection, interpretation, intervention piloting, and community-facilitated implementation.

Main Results:

  • The co-KT Framework operationalizes the translational cycle from local context to applicable interventions.
  • Research questions were framed by emergent data, ensuring relevance and community engagement.
  • Demonstrated a systematic approach to embedding KT principles throughout community-based research.

Conclusions:

  • The co-KT Framework provides a systematic method for integrating Knowledge Translation into all stages of community-based research.
  • It emphasizes local context as the foundation for knowledge creation and intervention development.
  • Applicable to KT research, participatory action research, population health, and health systems studies.