Article type
Abstract
Background
The purpose of using algorithmic models to automate human tasks is to decrease workload, costs, and time. One application of this technology is the automation of the systematic literature review (SLR) process while maintaining transparency and reproducibility. Opinions on the adoption and implementation of automation tools vary. Although the International organizations promote its adoption, some perspectives from experienced clinical practice guideline developers around the world, concern about the compatibility of new technologies with the quality and transparency that underpin practice.
Objectives
To develop the FAIR-GENE framework to automate evidence generation (GENE) according to the principles of Findability, Accessibility, Interoperability, and Reusability (FAIR), while ensuring rigor, integrity, transparency and reliability over time.
Methods
The objective of the project will be achieved through three phases. Phase 1 will consist of a scoping review to describe the scope of knowledge related to the internal and external validity of models for screening references in an SLR process. In Phase 2, the researchers will generate a theoretical definition of the FAIR-GENE framework and Will undergo a face-validity to assess the measure's appropriateness and relevance. During Phase 3; we will externally validate an algorithmic model generated after de scoping análisis using published Systematic Literature Review (SLR) datasets, where also we will assess the FAIR-GENE framework properties.
Expected Results
The expected outcomes of the project include the development and fase-validity of a framework called FAIR-GENE, which aims to support the decision process for automating evidence generation tasks by providing a structured approach.
Conclusions
A conceptual framework is needed to identify and evaluate the usability characteristics of the various models available today.
The purpose of using algorithmic models to automate human tasks is to decrease workload, costs, and time. One application of this technology is the automation of the systematic literature review (SLR) process while maintaining transparency and reproducibility. Opinions on the adoption and implementation of automation tools vary. Although the International organizations promote its adoption, some perspectives from experienced clinical practice guideline developers around the world, concern about the compatibility of new technologies with the quality and transparency that underpin practice.
Objectives
To develop the FAIR-GENE framework to automate evidence generation (GENE) according to the principles of Findability, Accessibility, Interoperability, and Reusability (FAIR), while ensuring rigor, integrity, transparency and reliability over time.
Methods
The objective of the project will be achieved through three phases. Phase 1 will consist of a scoping review to describe the scope of knowledge related to the internal and external validity of models for screening references in an SLR process. In Phase 2, the researchers will generate a theoretical definition of the FAIR-GENE framework and Will undergo a face-validity to assess the measure's appropriateness and relevance. During Phase 3; we will externally validate an algorithmic model generated after de scoping análisis using published Systematic Literature Review (SLR) datasets, where also we will assess the FAIR-GENE framework properties.
Expected Results
The expected outcomes of the project include the development and fase-validity of a framework called FAIR-GENE, which aims to support the decision process for automating evidence generation tasks by providing a structured approach.
Conclusions
A conceptual framework is needed to identify and evaluate the usability characteristics of the various models available today.