## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**IV Scores a**} & **Type of DV Score** & **DV Score b** & **DV Estimated Score** & **\%** & **OR** \\  & & **Probability** & **Probability** & **Probability** & & \\ \hline \multirow{4}{*}{1} & \(X_{1}=1\) & \multirow{4}{*}{cumulative} & \(P\) (Score \(\leq\) 1) & 0.888 & 88.77 & \multirow{4}{*}{0.132} \\  & \(X_{2}=1\) & \(P\) (Score \(\leq\) 2) & 0.984 & 98.36 & & \\  & \(X_{3}=1\) & & \(P\) (Score \(\leq\) 3) & 1 & 100 & \\ \cline{2-6}  & \(X_{4}=1\) & \multirow{4}{*}{individual} & \(P\) (Score \(=\) 1) & 0.888 & _88.77_ & \multirow{4}{*}{0.132} \\  & \(X_{5}=1\) & & \(P\) (Score \(=\) 2) & 0.096 & 9.59 & \\  & & & \(P\) (Score \(=\) 3) & 0.016 & 1.64 & \\ \hline \multirow{4}{*}{2} & \(X_{1}=2\) & \multirow{4}{*}{cumulative} & \(P\) (Score \(\leq\) 1) & 0.617 & 61.67 & \multirow{4}{*}{0.132} \\  & \(X_{2}=2\) & \multirow{4}{*}{\(X_{3}=2\)} & \multirow{4}{*}{\(P\) (Score \(\leq\) 3)} & 1 & 100 & \\ \cline{2-6}  & \(X_{4}=2\) & & \(P\) (Score \(=\) 1) & 0.617 & \multirow{4}{*}{0.132} \\  & \(X_{5}=2\) & \multirow{4}{*}{individual} & \(P\) (Score \(=\) 2) & 0.308 & 30.76 & \multirow{4}{*}{0.132} \\  & & & \(P\) (Score \(=\) 3) & 0.076 & 7.57 & \\ \hline \multirow{4}{*}{3} & \(X_{1}=3\) & \multirow{4}{*}{cumulative} & \(P\) (Score \(\leq\) 1) & 0.247 & 24.67 & \multirow{4}{*}{0.132} \\  & \(X_{2}=3\) & & \(P\) (Score \(\leq\) 2) & 0.713 & 71.32 & \\ \cline{2-6}  & \(X_{3}=3\) & & \(P\) (Score \(\leq\) 3) & 1 & 100 & \\ \cline{2-6}  & \(X_{4}=3\) & \multirow{4}{*}{individual} & \(P\) (Score \(=\) 1) & 0.247 & 24.67 & \multirow{4}{*}{0.132} \\  & \(X_{5}=3\) & & \(P\) (Score \(=\) 2) & 0.466 & 46.65 & \\ \cline{2-6}  & & & \(P\) (Score \(=\) 3) & 0.287 & 28.68 & \\ \hline \multirow{4}{*}{4} & \(X_{1}=4\) & \multirow{4}{*}{cumulative} & \(P\) (Score \(\leq\) 1) & 0.062 & 6.25 & \multirow{4}{*}{0.132} \\  & \(X_{2}=4\) & \multirow{4}{*}{\(X_{3}=4\)} & \multirow{4}{*}{\(P\) (Score \(\leq\) 2)} & 0.336 & 33.60 & \\ \cline{2-6}  & \(X_{3}=4\) & & \(P\) (Score \(\leq\) 3) & 1 & 100 & \\ \cline{2-6}  & \(X_{4}=4\) & \multirow{4}{*}{individual} & \(P\) (Score \(=\) 1) & 0.062 & 6.25 & \multirow{4}{*}{0.132} \\  & \(X_{5}=4\) & & \(P\) (Score \(=\) 2) & 0.274 & 27.35 & \\ \cline{2-6}  & \(X_{5}=3\) & & \(P\) (Score \(=\) 3) & 0.664 & 66.40 & \\ \hline \multirow{4}{*}{5} & \(X_{1}=5\) & \multirow{4}{*}{cumulative} & \(P\) (Score \(\leq\) 1) & 0.013 & 1.34 & \multirow{4}{*}{0.132} \\  & \(X_{2}=5\) & & \(P\) (Score \(\leq\) 2) & 0.093 & 9.34 & \\ \cline{2-6}  & \(X_{3}=5\) & & \(P\) (Score \(\leq\) 3) & 1 & 100 & \\ \cline{2-6}  & \(X_{4}=5\) & & \(P\) (Score \(=\) 1) & 0.013 & 1.34 & \multirow{4}{*}{0.132} \\ \cline{2-6}  & \(X_{4}=5\) & & \(P\) (Score \(\leq\) 3) & 1 & 100 & \\ \cline{2-6}  & \(X_{4}=5\) & & \(P\) (Score \(=\) 1) & 0.013 & 1.34 & \multirow{4}{*}{0.132} \\ \cline{2-6}  & \(X_{4}=5\) & & \(P\) (Score \(\leq\) 2) & 0.080 & 8.00 & \\ \cline{2-6}  & \(X_{5}=5\) & & \(P\) (Score \(=\) 3) & 0.907 & 90.66 & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Sensitivity analysis and scenario applications of the developed prediction model. Finally, the novelty of the prediction model developed in this study lies in the modeling process following the bootstrapped multiple ordinal logistic regression approach, which revealed five significant predictors that are part of the most significant determinants that cluster under the three main safety climate components: safety support, safety commitment, and safety interaction (Table 3). This underlines the importance of these three key components in construction sites and their impact on construction personnel's evaluations of the safety climate. Moreover, the developed prediction model provides an instrument that can help construction industry decision makers, safety policy designers, government agencies, and stakeholders to estimate the safety climate of construction sites and determine areas of improvement.