[R-meta] Help with interpretation please?

[Paul Cuckoo]

I'm using the metafor package in R to attempt to model the impact various
covariates have on an uplift score.

> head(data.test)
Cell.No. brand_lift_ppt experiment_variance           vertical_name
Gender age_targeting_min1     0.01002470         0.000251804
[Entertainment & Media]      A                352     0.01343524
  0.000119650 [Entertainment & Media]      M                183
0.01601813         0.000352114 [Entertainment & Media]      F
      184     0.17279558         0.000275272 [Entertainment & Media]
   F                185     0.14091904         0.000203351
[Entertainment & Media]      M                186     0.13449727
  0.000202170 [Entertainment & Media]      A                35

I specify the model as follows:

model1 <- rma(data=data.test, yi=brand_lift_ppt, vi=experiment_variance,
              mods = ~ vertical_name)
summary(model1)

and get the following output:

Mixed-Effects Model (k = 375; tau^2 estimator: REML)

    logLik    deviance         AIC         BIC        AICc
  500.9043  -1001.8086   -985.8086   -954.5439   -985.4075

tau^2 (estimated amount of residual heterogeneity):     0.0031 (SE = 0.0003)
tau (square root of estimated tau^2 value):             0.0558
I^2 (residual heterogeneity / unaccounted variability): 86.70%
H^2 (unaccounted variability / sampling variability):   7.52
Test for Residual Heterogeneity:
QE(df = 368) = 2703.1344, p-val < .0001
Test of Moderators (coefficient(s) 1:7):
QM(df = 7) = 253.9762, p-val < .0001
Model Results:

                                        estimate      se     zval
pval    ci.lb   ci.ub
vertical_name[Automotive]                 0.0398  0.0081   4.9171
<.0001   0.0239  0.0556  ***
vertical_name[Consumer Packaged Goods]    0.0510  0.0050  10.1589
<.0001   0.0412  0.0609  ***
vertical_name[Entertainment & Media]      0.0642  0.0071   9.0057
<.0001   0.0502  0.0782  ***
vertical_name[Retail]                     0.0559  0.0087   6.4059
<.0001   0.0388  0.0730  ***
vertical_name[Technology]                 0.0063  0.0335   0.1875
0.8513  -0.0593  0.0719
vertical_name[Telecom]                    0.0226  0.0112   2.0273
0.0426   0.0008  0.0445    *
vertical_name[Travel]                     0.0239  0.0428   0.5580
0.5768  -0.0600  0.1078

I'm having trouble interpreting the output to get to what I need. Some
questions:

1) Entertainment and Media appears to be significant. How do I interpret
the coefficient? I haven't logged or otherwise transformed any of the data.
Is it simply that when I select Entertainment and Media, I see on average a
6.42% brand uplift?

2) How can I get an overall estimate of the impact of vertical media,
relative to other covariates, say gender? A ratio of QM score?

3) Should I be combining all factors together in mods or is it acceptable
to test separately?

4) Under what circumstances should I be considering a transformation to
brand_lift before modelling?

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