<![CDATA[The textile industry plays a vital role in supporting the national economy, but is characterized by complex and hazardous working conditions that pose serious challenges to occupational health and safety (OHS). Workers are frequently exposed to high-speed machinery, harmful chemicals, excessive dust, and physically demanding tasks, making risk identification and prioritization essential for improving workplace safety. This study aims to systematically identify and rank the most critical OHS risk factors by employing a hybrid methodology that integrates Exploratory Factor Analysis (EFA) and the Grey Delphi method. Data were collected from 390 textile workers and subsequently validated through the consensus of 12 experts. The EFA process reduced 57 initial indicators into nine underlying categories, while the Grey Delphi analysis prioritized 25 risks. Among these, the five most critical risks identified are: (1) excessive noise generated by weaving and spinning machines, (2) exposure to cotton dust containing endotoxins, (3) unprotected moving machine parts, (4) long working hours without adequate rest, and (5) improper or inconsistent use of personal protective equipment (PPE). The novelty of this study lies in integrating quantitative factor reduction with expert consensus under uncertainty, producing a replicable hybrid framework for data-driven OHS risk prioritization. This approach advances current literature by bridging statistical analysis with expert judgment, thereby improving methodological rigor. The findings provide measurable contributions for both scholars and practitioners by offering evidence-based guidance for policy formulation, resource allocation, and the design of targeted safety interventions to enhance OHS management in the textile sector.]]>
