Ordinal response with monotonic predictor

Interfaces brms
25

Hi,
For three models as below I ran loo_compare:

p2<-get_prior( rec_n_r_bodn ~ mo(rec_comb_sevchf)+(1|idno),family=cumulative(),
              data =bodi)

p2$prior[1] <- "normal(0,1)" # sets a N(0,1) on \beta
p2$prior[3] <- "normal(0,100)" # for each cutpoint
p2$prior[18] <- "cauchy(0,2.5)" # for sd
p2$prior[21] <- "dirichlet(2,2)" # sets weakly regularizing dirichlet for 1-3 Likert scale response

fit_bodn2<-brm(
  formula = rec_n_r_bodn~ mo(rec_comb_sevchf)+(1|idno),family=cumulative,
  data =bodi,prior=p2,chain=5,thin=1,control = list(adapt_delta =0.99,max_treedepth=15))

p3<-get_prior( rec_n_r_bodn ~ rec_comb_sevchf+(1|idno),family=cumulative(),
               data =bodi)

p3$prior[1] <- "normal(0,1)" # sets a N(0,1) on \beta
p3$prior[3] <- "normal(0,100)" # for each cutpoint
p3$prior[18] <- "cauchy(0,2.5)" # for sd

fit_bodn3<-brm(
  formula = rec_n_r_bodn~ rec_comb_sevchf+(1|idno),family=cumulative,
  data =bodi,prior=p3,chain=5,thin=1,control = list(adapt_delta =0.99,max_treedepth=15))

p4<-get_prior( rec_n_r_bodn ~ (1|idno),family=cumulative(),
               data =bodi)

p4$prior[1] <- "normal(0,1)" # sets a N(0,1) on \beta
p4$prior[16] <- "cauchy(0,2.5)" # for sd


fit_bodn4<-brm(
  formula = rec_n_r_bodn~ (1|idno),family=cumulative,
  data =bodi,prior=p4,chain=5,thin=1,control = list(adapt_delta =0.99,max_treedepth=15))

fit2 ← add_criterion(fit_bodn2, criterion = “loo”,reloo = TRUE)
fit3 ← add_criterion(fit_bodn3, criterion = “loo”,reloo = TRUE)
fit4 ← add_criterion(fit_bodn4, criterion = “loo”,reloo = TRUE)

loo_compare(fit2, fit3, fit4)

model_weights(fit2, fit3, fit4, weights = "loo")
        fit2         fit3         fit4 
9.013943e-01 9.860567e-02 6.967917e-48 

loo_compare(fit2, fit3, fit4)
     elpd_diff se_diff
fit2    0.0       0.0 
fit3   -2.2       4.0 
fit4 -108.5      15.9 
Computed from 5000 by 2622 log-likelihood matrix

         Estimate   SE
elpd_loo  -4368.7 46.4
p_loo       900.7 21.1
looic      8737.4 92.8
------
Monte Carlo SE of elpd_loo is NA.

Pareto k diagnostic values:
                         Count Pct.    Min. n_eff
(-Inf, 0.5]   (good)     2375  90.6%   263       
 (0.5, 0.7]   (ok)        213   8.1%   89        
   (0.7, 1]   (bad)        21   0.8%   18        
   (1, Inf)   (very bad)   13   0.5%   5   
omputed from 5000 by 2622 log-likelihood matrix

         Estimate   SE
elpd_loo  -4366.5 46.6
p_loo       902.9 21.5
looic      8733.0 93.2
------
Monte Carlo SE of elpd_loo is NA.

Pareto k diagnostic values:
                         Count Pct.    Min. n_eff
(-Inf, 0.5]   (good)     2389  91.1%   282       
 (0.5, 0.7]   (ok)        193   7.4%   149       
   (0.7, 1]   (bad)        29   1.1%   18        
   (1, Inf)   (very bad)   11   0.4%   4         
See help('pareto-k-diagnostic') for details.

Can I interpret that model 2 is better as it has better weight and regarding LOO_ic model 2 and 3 are almost the same?
I read these posts as well but a bit lost in interpretation:

Thanks