Rstudio metafor moderator analysis: one variable is missing in Model Results + eliminating intrcpt?

Question

I am currently working on a meta analysis and right now I am trying to do a moderator/moderation analysis but I have come across one problem: When I try to run the function:

MA_SampleType <- rma(yi, vi, mods = ~ SampleType)

I can only see four of my five variables in the Model Results. The same problem occurs for all my other variables and in the regression analysis and the meta- regression as well. Now I have been trying to find a solution and that has been getting rid of the intercept like this:

MA_SampleType <- rma(yi, vi, mods = ~ SampleType -1)

now without the intercept my results (mostly regarding p-value) are very different and I am unsure whether or not I should remove the intercept, which is why I am hoping for your input. Do I need the intercept at all?

For clarification: this is a meta analysis on the effects of psycho-therapeutic interventions on children, so there is no "Null" possibility in my data, all effect sizes are directly linked to an intervention, a therapy setting, etc.

Based on my Google research I have already tried to remove the intercept from the Model Results but I don't know if I should keep the intercept or if it is not a problem to remove it in regards to the validity of my data. Also I have not found a way to include all my variables (in the case of SampleType that would be 5) and the intercept.

With intercept:

QM(df = 4) = 11.5171, p-val = 0.0213

Model Results:

                              estimate      se     zval    pval    ci.lb    ci.ub      
intrcpt                         3.2570  0.8764   3.7162  0.0002   1.5392   4.9747  *** 
SampleTypeintervention_group   -1.6782  0.9025  -1.8595  0.0630  -3.4469   0.0906    . 
SampleTypeno_treatment         -2.8170  0.9569  -2.9440  0.0032  -4.6924  -0.9416   ** 
SampleTypeother_treatment      -1.7642  0.9320  -1.8930  0.0584  -3.5908   0.0624    . 
SampleTypepharmacotherapy      -1.7374  1.2259  -1.4172  0.1564  -4.1401   0.6653      

without intercept:

QM(df = 5) = 94.2744, p-val < .0001

Model Results:

                              estimate      se    zval    pval    ci.lb   ci.ub      
SampleTypeCBT_pharmaco          3.2570  0.8764  3.7162  0.0002   1.5392  4.9747  *** 
SampleTypeintervention_group    1.5788  0.2152  7.3362  <.0001   1.1570  2.0006  *** 
SampleTypeno_treatment          0.4400  0.3840  1.1458  0.2519  -0.3126  1.1926      
SampleTypeother_treatment       1.4927  0.3169  4.7105  <.0001   0.8716  2.1138  *** 
SampleTypepharmacotherapy       1.5196  0.8571  1.7729  0.0763  -0.1604  3.1996    .