Only the last variable is statistically significant (_cons). Its basically impossible to interpret substantively slopes in logistic regression other than their sign and if they are significant. They refer to a change in the logit for a one unit change in X controlling for other predictors. Not many understand what that means
You need to request odds ratios.
Everyone is stupid here except Dason... and he is a robot
The slopes and the intercept are not so intuitive like linear regression but can still be interpreted.
The screenshot is not so clear. under the assumption that mer_ng_ml is the intercept. (or just for the example)
When all the values of the predictors (Xj) are zero:
The odds of Y(1) in comparison to Y(0) is: 1.09825955
One unit increase in X1:
Will increase the odds of Y(1) in comparison to Y(0) by 9.8% (a.k.a. the odds will be multiplied by 1.09825955).