Soumya Choudhary computation of effect size is based upon a pooled variance or an adjusted variance. Skeptical tendencies may occur in response to this practice, as meta-analysis is illusory. Meta-analyses use randomized and observational studies. In the former, it is usually the case that subjects are not drawn at random from populations with a common variance. In observational studies there is no randomization at all. Thus, it is gratuitous to assume that standardized effects are constant across studies.

Based on the information that you've given, I think that a meta-analysis is not meaningfully possible in this case (unless you can obtain the actual quantitative outcome data of interest from the authors of the included papers). Perhaps a systematic review without meta-analysis is a good option. Qualitative synthesis of the evidence is truly as important as the more "exciting" quantitative counterpart. Don't force yourself in going straight to making a meta-analysis just for its sake.

Soumya Choudhary computation of effect size is based upon a pooled variance or an adjusted variance. Skeptical tendencies may occur in response to this practice, as meta-analysis is illusory. Meta-analyses use randomized and observational studies. In the former, it is usually the case that subjects are not drawn at random from populations with a common variance. In observational studies there is no randomization at all. Thus, it is gratuitous to assume that standardized effects are constant across studies.

A few options: 1) email authors asking them for the info you need or the raw data set; 2) calculate effect sizes from info available (if possible; I recommend this resource: https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/effectsizecalculator.html); 3) exclude study (lack of info to calculate effect sizes is a common reason for exclusion in MA). Hope this helps!