Interpretation of the results of a multi-level meta-analysis with metafor

Question

I have noticed that the topic has been already treated in other questions but I am still struggling about one specific issues. In details,

FC_Feedstock <- rma.mv(yi = EF, 
+                      V = STDEV, 
+                      slab = N,
+                      data = FC,
+                      random = ~ 1 | Title/N, 
+                      test = "t", 
+                      method = "REML", mods = ~ Feedstock)
> summary(FC_Feedstock)

Multivariate Meta-Analysis Model (k = 19; method: REML)

  logLik  Deviance       AIC       BIC      AICc   
 -2.1142    4.2284   12.2284   15.5612   15.5617   

Variance Components:

            estim    sqrt  nlvls  fixed   factor 
sigma^2.1  0.0523  0.2287      6     no    Title 
sigma^2.2  0.0429  0.2071     19     no  Title/N 

Test for Residual Heterogeneity:
QE(df = 17) = 274.3639, p-val < .0001

Test of Moderators (coefficient 2):
F(df1 = 1, df2 = 17) = 0.3250, p-val = 0.5761

Model Results:

                   estimate      se     tval  df    pval    ci.lb   ci.ub    
intrcpt             -0.0555  0.1952  -0.2844  17  0.7796  -0.4675  0.3564    
Feedstocksoftwood   -0.1149  0.2015  -0.5701  17  0.5761  -0.5400  0.3102    

---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

I understand that the effect size for the category "softwood" of the moderator "feestock" is "-0.0555 + (-0.1149)", that is -0.1704, but the question is if also the confidence intervals should be derived summing them to the ones of the intercept or if instead they are already reported in the "definitive" form. In a few words the effects size and confidence intervals for the category "softwood" are:

-0.1704 [-0.54, 0.3102] or -0.1704 [-1.0075, 0.6666]

Answer 1

I would propose just to reverse the order of the factor levels for the column Feedstock in your original dataset (let's call this original dataset df):

if (!require('tidyverse')) install.packages('tidyverse'); library('tidyverse')
df <- df %>% mutate(Feedstock = fct_rev(Feedstock))

and then to take the intercept's CI. If the output of the model doesn't change, then to run the line

df <- df %>% mutate(Feedstock = fct_rev(Feedstock))

one more time and repeat modeling.