There are different approaches for developing programmatic interventions that have been described. One of them is the research-to-practice model which generally includes research from the onset and follows predefined stages for program development². Another approach is the community-centered model².

A new concept called “scientific accompaniment” has been proposed to expand and guide program development and testing¹. It aims to argue that a singular focus on evaluation has limited the ways in which science and research are incorporated into program development¹.

In addition, multiscale modeling is a successful strategy to integrate multiscale, multiphysics data and uncover mechanisms that explain the emergence of function³.

Source:

(1) Scientific accompaniment: a new model for integrating program .... https://www.emerald.com/insight/content/doi/10.1108/JCS-09-2021-0037/full/pdf?title=scientific-accompaniment-a-new-model-for-integrating-program-development-evidence-and-evaluation.

(2) Scientific accompaniment: a new model for integrating program .... https://www.emerald.com/insight/content/doi/10.1108/JCS-09-2021-0037/full/html.

(3) Integrating machine learning and multiscale modeling ... - Nature. https://www.nature.com/articles/s41746-019-0193-y.

[1]Rameshwar Gupta*,

Research Scholar,

Department of Lifelong Learning & Extension

CSJM University, Kanpur, U.P. India

Email id: [email protected]

Whatsapp/Mobile: +918630831266

https://orcid.org/0000-0003-3761-3591

[1]Doctoral Student, Department of Lifelong Learning & Extension, CSJM University, Kanpur, U.P., India, Email id: [email protected], Mobile: 8630831266, https://orcid.org/0000-0003-3761-3591

*Single author

To develop a new model by integrating two scientific areas, follow these steps:

Identify the areas to integrate.

Review literature for connections.

Define scope and objectives.

Identify integration points.

Develop a conceptual framework.

Design and conduct research.

Refine the model based on results.

Validate through peer review or applications.

Document the process and findings.

The research-to-practice model involves conducting research in one scientific area and then using the findings of that research to develop a model in another scientific area . There are many different methodologies that can be used to develop a new model by integrating two scientific areas.

Soft systems mythology complemented with VSM in step number 4

Guideline for the development process:

1. Identify the Scientific Areas: Clearly identify the two scientific areas that you intend to integrate. Understand the fundamental concepts, theories, and existing models within each area. This will help you identify potential synergies and areas for integration.

2. Define the Objectives: Clearly define the objectives of the new model. What problem or phenomenon are you aiming to address or explain? Determine the specific aspects of each scientific area that you want to combine and the desired outcomes of the integrated model.

3. Literature Review: Conduct a thorough literature review across both scientific areas to gain a comprehensive understanding of the existing research, models, and theories. Identify any previous attempts at integration, as well as gaps and limitations that your new model can address.

4. Conceptual Framework: Develop a conceptual framework that outlines the key concepts, variables, and relationships that will form the basis of your integrated model. This framework should reflect the integration of the two scientific areas and provide a clear structure for the model development process.

5. Model Design: Based on the conceptual framework, design the structure and components of the integrated model. Determine the mathematical or computational approach that will be used, considering the specific requirements and characteristics of both scientific areas. Define the equations, algorithms, or rules that govern the interactions and dynamics within the model.

6. Model Implementation: Implement the model using appropriate programming languages or software tools. Ensure that the integration of the two scientific areas is properly reflected in the code and that the model behaves as intended.

7. Calibration and Validation: Calibrate the model parameters by comparing the model outputs with real-world data or existing empirical studies. Validate the model by testing its performance against independent datasets or conducting sensitivity analyses. Iteratively refine the model based on the calibration and validation results.

8. Model Evaluation and Documentation: Evaluate the performance of the integrated model against the defined objectives. Assess its strengths, limitations, and applicability in different contexts. Document the model development process, including assumptions, methodologies, and data sources used, to facilitate transparency and reproducibility.

9. Dissemination and Collaboration: Share your integrated model with the scientific community through publications, presentations, or open-source platforms. Seek feedback and collaborate with experts from both scientific areas to enhance the model and explore further applications.

Hope it helps