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

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 am trying 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 intrcpt 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.

Thank you so much for your help and time!

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 intrcpt:

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

Model Results:

                              estimate      se     zval    pval    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 intrcpt:

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

Model Results:

                              estimate      se    zval    pval   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    . 

All that's going on is that in the first result SampleTypeCBT_pharmaco is being treated as the reference group and the other coefficients are showing deviations from the reference group. In the second result the coefficients are in levels.

Note that the prediction for any group is identical in both sets of output.

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