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

Modeling and Similitude01:12

Modeling and Similitude

677
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
677
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

360
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
360
Modeling with Differential Equations01:25

Modeling with Differential Equations

114
Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
114
Modeling in Therapy01:26

Modeling in Therapy

572
Modeling, a key technique in therapy, uses observational learning to help clients acquire and practice new skills by watching therapists demonstrate desired behaviors. This approach, rooted in Albert Bandura's concept of vicarious learning, plays a significant role in therapeutic interventions for various psychological conditions, including social anxiety, ADHD, and depression.
Participant Modeling
Participant modeling involves therapists demonstrating calm and effective behaviors in...
572
Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model01:14

Pharmacodynamic Models: Link Model and Systems Pharmacodynamic Model

30
The link model is a fundamental pharmacokinetic-pharmacodynamic (PK–PD) approach to account for delayed drug responses when the observed effect does not immediately correlate with the drug's plasma concentration peak. This delay is mathematically addressed by introducing an effect compartment concentration, Ce, which is kinetically linked to the plasma concentration, Cp, via a first-order rate constant, ke0. The linkage allows for a more accurate prediction of drug effects over time. A...
30
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

290
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
290

You might also read

Related Articles

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

Sort by
Same author

Using machine learning-based Natural Language Processing to quantify emergency department presentations related to suicide or self-harm in the Australian Capital Territory.

The Australian and New Zealand journal of psychiatry·2026
Same author

Tiny Bites, a digital health intervention delivered in early childhood education and care centres to support educators and caregivers to prevent childhood obesity: study protocol for a cluster randomised controlled trial.

BMJ open·2025
Same author

A Conserved N-Terminal Di-Arginine Motif Stabilizes Plant DGAT1 and Modulates Lipid Droplet Organization.

International journal of molecular sciences·2025
Same author

Toward sustainable crops: integrating vegetative (non-seed) lipid storage, carbon-nitrogen dynamics, and redox regulation.

Frontiers in plant science·2025
Same author

Lipid storage in green tissues alters redox homeostasis, malate metabolism, phospholipids, and nitrogen partitioning in plants.

Plant physiology and biochemistry : PPB·2025
Same author

Monitoring privilege for health equity: building consensus on indicators to monitor socioeconomic advantage through a modified Delphi survey.

Social science & medicine (1982)·2025

Related Experiment Video

Updated: Feb 21, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K

Knowledge mobilisation for policy development: implementing systems approaches through participatory dynamic

Louise Freebairn1,2,3, Lucie Rychetnik4,5, Jo-An Atkinson4,6

  • 1ACT Government, Health Directorate, GPO Box 825, Canberra, ACT, 2601, Australia. louise.freebairn@act.gov.au.

Health Research Policy and Systems
|October 4, 2017
PubMed
Summary

Participatory simulation modeling effectively mobilizes diverse evidence for health policy decisions. This approach integrates research, expert knowledge, and context, empowering decision-makers in policy development.

Keywords:
AlcoholChildhood obesityDecision supportDiabetes in pregnancyKnowledge mobilisationParticipatory dynamic simulation modelling

More Related Videos

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.2K

Related Experiment Videos

Last Updated: Feb 21, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.3K
Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
20:36

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

Published on: July 4, 2007

9.2K

Area of Science:

  • Health Policy and Systems Research
  • Knowledge Mobilization Science
  • Simulation Modeling and Systems Science

Background:

  • Effective health policy and service planning rely on evidence-based decision-making, yet integrating diverse evidence forms remains challenging.
  • Knowledge mobilization for policy and practice is a complex, relational, and context-dependent process.
  • Systems approaches, like dynamic simulation modeling, can analyze complex health issues and contexts, aiding decision support.

Purpose of the Study:

  • To report the novel application of participatory simulation modeling as a knowledge mobilization tool in Australian policy settings.
  • To describe the integration of systems science methodology with knowledge mobilization best practices.
  • To detail strategies for addressing technical and socio-political challenges in three case studies and derive lessons learned.

Main Methods:

  • Utilized participatory dynamic simulation modeling, combining systems science with knowledge mobilization principles.
  • Implemented strategies to navigate technical and socio-political complexities in real-world policy environments.
  • Employed deliberative methods and co-production of knowledge, centering decision-makers in the process.

Main Results:

  • Demonstrated the feasibility of participatory simulation modeling in Australian policy development settings.
  • Successfully integrated diverse evidence, including research, expert knowledge, and local context, into decision support tools.
  • Facilitated collaboration among health stakeholders to explore policy and health service scenarios.

Conclusions:

  • Participatory dynamic simulation modeling enhances health stakeholder collaboration for public health priority topics.
  • Simulation models serve as dynamic decision support tools, integrating varied evidence for policy and programs.
  • The participatory approach embeds decision-makers, promoting co-production of knowledge and informed policy development.