What does the output of flexsurvreg, for a Gompertz proportional hazards model, mean?

I am estimating a Gompertz proportional hazards model in R using the package "flexsurvreg", but I'm having a hard time understanding the output of this function. My dataset is collected from skeletal remains, so my event is estimated age at death and my explanatory variable in community dwelling- either in monastic or urban communities during the medieval period in the UK. In the past, I've used the Cox proportional hazards model from the "survival" package, but the standard in my field is a Gompertz model. The problem is that the output of flexsurvreg is different than the coxph function so I don't know how to interpret the results.

Here's the output from flexsurvreg:

Call:
flexsurvreg(formula = Surv(age) ~ monastery, data = dat, dist = "gompertz")

Estimates:
data mean  est       L95%      U95%      se        exp(est)  L95%
shape                 NA   0.072851  0.069204  0.076498  0.001861        NA        NA
rate                  NA   0.002167  0.001817  0.002584  0.000194        NA        NA
monasteryUrban  0.322643   0.338239  0.205303  0.471174  0.067826  1.402475  1.227897
U95%
shape                 NA
rate                  NA
monasteryUrban  1.601874

N = 1029,  Events: 1029,  Censored: 0
Total time at risk: 42770.5
Log-likelihood = -4113.269, df = 3
AIC = 8232.537

And here's the output from coxph on the same data

Call:
coxph(formula = Surv(age) ~ monastery, data = dat)

n= 1029, number of events= 1029

coef exp(coef) se(coef)    z Pr(>|z|)
monasteryUrban 0.21742   1.24286  0.06753 3.22  0.00128 **
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

exp(coef) exp(-coef) lower .95 upper .95
monasteryUrban     1.243     0.8046     1.089     1.419

Concordance= 0.531  (se = 0.011 )
Likelihood ratio test= 10.11  on 1 df,   p=0.001
Wald test            = 10.37  on 1 df,   p=0.001
Score (logrank) test = 10.41  on 1 df,   p=0.001

For the flexsurvreg output, how do I interpret the overall performance of the model- is log-likelihood the same thing as a likelihood ratio test? How can I tell whether the hazards between my two groups (monastic vs. urban dwellers) are significantly different?