Predicting public health impact: Linking ResearchGate presence to Scopus performance through machine learning

Area of Science:

• Occupational Health Engineering
• Scientometrics
• Bibliometrics

Background:

• ResearchGate and Scopus are key platforms for scientific output dissemination and citation tracking.
• Evaluating researcher performance is crucial for understanding scientific productivity and impact.

Purpose of the Study:

• To assess the performance of Iranian researchers in occupational health using ResearchGate and Scopus metrics.
• To correlate researcher performance with variables such as social media activity and citation counts.
• To employ machine learning (regression analysis) for predicting scientific performance.

Main Methods:

• A descriptive, cross-sectional study conducted in 2024.
• Data collected from 213 Iranian researchers in Occupational Health Engineering from ResearchGate and Scopus.
• Demographic information sourced from the Iranian Scientometrics Information Database.

Main Results:

• 70% of researchers were active on ResearchGate, with a mean RG score of 11.70 and 28.89 shared items per researcher.
• Scopus data revealed 93% of researchers had 2,935 published documents and 18,749 citations.
• A positive correlation was found between ResearchGate activity and Scopus performance; ResearchGate presence predicted Scopus citation counts (R²=.82) and h-indexes (R²=.83).

Conclusions:

• Iranian occupational health researchers effectively leverage ResearchGate, influencing their research output.
• Encouraging greater interaction on social media platforms can amplify the reach and impact of scientific work.