RPRIT-Based Safety Index Mode

The **RPRIT-Based Safety Index Model** is a conceptual and psychometric framework designed to measure psychological safety consciousness through the lens of Rabindrik Psychotherapy. The model integrates three interrelated layers of human functioning—Murta, Raag, and Saraswat—which are derived from Rabindranath Tagore’s value-based philosophy of life and consciousness. Each of these layers captures a unique dimension of how individuals internalize, respond to, and uphold safety within industrial environments.

The **Murta layer** refers to the physical and behavioral domain of safety, emphasizing habit formation, routine compliance, and sensory alertness. This layer captures the observable acts of safety such as consistently wearing protective equipment, strictly following safety protocols, and maintaining attentiveness during shifts. The Murta layer is associated with behavioral indicators of discipline and compliance, which are foundational to workplace safety.

The **Raag layer** encompasses the affective and emotional resonance individuals have toward safety. It reflects the emotional investment in one’s own safety and that of others, empathy for colleagues, and the value one places on human life. Workers scoring high on the Raag dimension are often those who feel discomfort when safety rules are violated and who actively support and motivate their peers in upholding safety norms. This layer captures the emotional and cultural climate around safety.

The **Saraswat layer** represents the most abstract and cognitive level of safety consciousness. It includes intuitive judgment, moral reasoning, ethical responsibility, and the capacity to reflect on the long-term implications of unsafe practices. Saraswat is the layer where wisdom, foresight, and philosophical understanding converge to influence safety-related decisions. Individuals operating strongly in this layer tend to be creative in hazard identification and proactive in suggesting improvements to safety mechanisms.

The **Safety Index (SI)** is derived by calculating weighted scores for each of these three layers. Each layer contributes a specific proportion to the final index score. The recommended weight distribution is 0.40 for Murta, 0.30 for Raag, and 0.30 for Saraswat. This distribution acknowledges that while behavioral compliance is critical, emotional commitment and moral insight are equally vital for sustaining a high safety culture. The formula is represented as:

SI = (0.40 × Murta Score) + (0.30 × Raag Score) + (0.30 × Saraswat Score)

Each of the three scores is obtained from psychometric assessments, typically involving Likert-scale items, where participants respond to statements reflective of their behaviors, feelings, and thoughts related to safety. For example, the Murta layer may include statements like “I always wear safety gear without being reminded” or “I avoid shortcuts in safety procedures.” The Raag layer may include items such as “I feel distressed when others ignore safety rules” or “I encourage peers to act safely.” The Saraswat layer may have items like “I reflect on how my actions can cause accidents” or “I feel morally responsible for maintaining safety in my unit.”

After administering the questionnaire, individual scores for Murta, Raag, and Saraswat are calculated on a uniform scale, often 0 to 100. The overall Safety Index is then computed using the weighted formula, providing a single score that reflects the integrated safety consciousness of an individual or team. For instance, if an individual scores 82 in Murta, 75 in Raag, and 88 in Saraswat, the Safety Index would be: SI = (0.40 × 82) + (0.30 × 75) + (0.30 × 88) = 81.7.

This index serves multiple practical purposes. It can be used to classify workers into categories such as low, moderate, or high safety consciousness, enabling targeted safety interventions. It also provides a psychological profile for tailoring safety training programs, designing motivational tools, and improving organizational safety culture. Additionally, the Safety Index can be incorporated into machine learning algorithms to predict accident risk or identify latent safety profiles among workers.

In essence, the RPRIT-Based Safety Index Model is a comprehensive, human-centered tool that brings together behavior, emotion, and consciousness in a unified psychometric framework. It offers a novel, value-oriented approach to industrial safety assessment, emphasizing not only what workers do, but also what they feel and how they think about safety.