Assistance with scaling constants and prior specifications in mirt

[YogM]

What is the scaling constant value in mirt, and do we need to rescale the parameters after estimation to match those from Parscale? If so, should we multiply or divide the estimated parameters by the scaling constant (e.g., 1.702)? I’m asking because when I multiply the values, they become even larger, and I already observe higher parameter values compared to Parscale. Also, I would like to know which parameters need to be rescaled—for example, should we rescale a, b, c, and their corresponding standard errors (a_se, b_se, c_se)?
Additionally, how can we apply priors in mirt to mimic the Parscale-like priors and get closer delta values? I have tried applying priors using the pars = custom_values approach through mod2values(). There is also the model_syntax method using PRIOR =, but I noticed that this method gives higher parameter values compared to the mod2values() approach. Furthermore, when I tried applying a beta prior using the mod2values() method, I received warnings and obtained suboptimal estimates, so I switched to using a normal prior instead.
For the calibration fit index, I have used the G2 RMSEA value. I would like to confirm if this is the correct approach. Lastly, could you please tell me the acceptable value ranges for the following parameters and statistics: a, b, c, a_se, b_se, c_se, ICCs (1, 2, 3), IIFs (1, 2, 3), and the calibration fit index? I would really appreciate your assistance with these queries.