Interpreting quadratic trend coefficients in a repeated measures logistic regression

I conducted an experiment where people either endorsed or did not endorse a statement at the commencement of a one-week training session, at the end of the one-week training session, and at a follow-up interview one week later. I hypothesised that the odds of endorsing the statement would increase between pre-training, and post-training measurement, but would decrease between post-training and follow-up. Here is the graph of the proportions endorsing the statement at each time point (pre-training = pre, post-training = post, follow-up = fu).

I turned the three-level time predictor into an ordered factor (pre < post < fu) in order to estimate linear and quadratic trends across the three time points and ran a repeated-measures logistic regression (i.e. a mixed effects model).

Here is the output of that analysis

Estimate Std.Err z-value  p-value
(Intercept)   2.9262  0.5042  5.8036  < 1e-04
timeOrd.L     0.8594  0.4039  2.1278 0.033354
timeOrd.Q    -1.6452  0.7628 -2.1566 0.031034

After exponentiating the log-odds coefficients and confidence intervals these are the results.

or lowCI   hiCI     p
linear trend          2.36  1.07   5.21 0.033
quadratic trend       0.19  0.04   0.86 0.031

So for the linear trend I gather the interpretation would be "the odds of endorsing the statement at each time point increase an estimated 2.36 times, compared to the previous time point."

However, I am struggling with how to interpret the quadratic trend. This post has some clues but that is for a model where the time predictor is continuous rather than a factor with three levels.