Unfortunately, there are a lot of reasons behind the messages you get here. It could be that the model is too complicated for the data. If you have a low overall response rate, that could be the reason.
Look at the iteration log. If the process is steadily marching towards convergence, then perhaps you can tweak some options to get it there. If the iteration log is bouncing all over the place, then you will need to simplify the model.
Try a simple model to start, say with just Gender and the RANDOM statement. Then add your next most interesting factor if that simple model converges. In the mixed model world, it's always best to start simple and work up from there.
If a model does not converge, that is not necessarily a bad thing. The procedure can be telling you that your model with your data are just not compatible.
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