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This research delves into the intricate landscape of Musculoskeletal Disorder
(MSD) risk factors, employing a novel fusion of Natural Language Processing
(NLP) techniques and mode-based ranking methodologies. The primary objective is
to advance the comprehension of MSD risk factors, their classification, and
their relative severity, facilitating more targeted preventive and management
interventions. The study utilizes eight diverse models, integrating pre-trained
transformers, cosine similarity, and various distance metrics to classify risk
factors into personal, biomechanical, workplace, psychological, and
organizational classes. Key findings reveal that the BERT model with cosine
similarity attains an overall accuracy of 28%, while the sentence transformer,
coupled with Euclidean, Bray-Curtis, and Minkowski distances, achieves a
flawless accuracy score of 100%. In tandem with the classification efforts, the
research employs a mode-based ranking approach on survey data to discern the
severity hierarchy of MSD risk factors. Intriguingly, the rankings align
precisely with the previous literature, reaffirming the consistency and
reliability of the approach. ``Working posture" emerges as the most severe risk
factor, emphasizing the critical role of proper posture in preventing MSDs. The
collective perceptions of survey participants underscore the significance of
factors like "Job insecurity," "Effort reward imbalance," and "Poor employee
facility" in contributing to MSD risks. The convergence of rankings provides
actionable insights for organizations aiming to reduce the prevalence of MSDs.
The study concludes with implications for targeted interventions,
recommendations for improving workplace conditions, and avenues for future
research.