How should I interpret the generalized squared multiple correlation? I am testing this model in SPSS AMOS.

The value of .23 above the top right corner of timedrs is the squared multiple correlation for that variable. 
I also ran the same analysis as two multi-step regressions. The results came out like this:


The generalized squared multiple correlation is described by Schumacker & Lomax (2004) on p159 as a "traditional non-SEM path model-fit [index]." The relevant text is as follows: 

Applying the formula for a generalized squared multiple correlation, I get:
1 – (1 - .119) × ( 1 - .227) =  0.32. 
This is higher than the .23 I obtained from the path analysis run in AMOS, and from the equation I can see that it can never be lower than the smallest value of R Square that goes into its calculation. I understand that I should not be surprised that the values are not the same. However, I am unsure about how to interpret the generalized squared multiple correlation, i.e. this $R^2m$ thing. What would a high/low generalized squared multiple correlation mean? Is it a good method of assessing model fit?
Schumacker, R. E., & Lomax, R. G. (2004). A beginner's guide to structural equation modeling. Psychology Press.
 A: Although as far as I can tell the 3rd edition of Schumacker & Lomax doesn't answer my question, the 4th edition (from 2015) does! Quoting p84 of that text (but changing the figure to match my data), the answer to the question is:
"The $R^2m$ for the path model would suggest that [32%] of the variance in [timedrs] is explained by the relations in the path model."
I'd still welcome further explanation of how this should be interpreted, but I'll take this as a good enough answer for now.
A: You're making a serious mistake by using estimation shown. You're pointing path model on graph, which can not be done in the SPSS, and SPSS output instead of AMOS output. The SPSS for which you specify showed tables can not execute the path-model as You showed, a simple regression or even multiple regressions outpu can not be combined in this way.
In addition, using a multiple regression adjusted R-squared instead of the usual R-squared to the percentage of the explained variance.
The rest of the discussion here does not make sense.
