To expand and complement [Frank Harrell's answer](https://stats.stackexchange.com/a/653342/73129):

**Providing an answer to the binary question based on the full model**

First, I think there is value in giving your collaborator a bit of pushback on requiring the answer to the binary question. On of the good reasons is that the binary question can be interpreted as several possible questions about the full dataset. The difference is about what assumptions do you make about the relative distribution of the "low" categories (1 vs 2 vs 3) and the relative distribution of the "high" categories (4 vs 5).

Inferences using the coefficient for the binarized predictor effectively assume that the relative frequencies in each category will be the same in the target population/future data as in the observed data. So this is a question you can answer using predictions from the full model (basically you weigh effect for each level of the full predictor by its frequency in the data). You can also get fancy and account for the uncertainty in the relative frequencies.

Another possible interpretation is to find a either a worst case or a best case estimate of a difference between the "low" and "high" categories across all possible relative frequencies within each category. This translates to either just comparing levels 3 and 4 (worst case) or levels 1 and 5 (best case) and can be answered with the full model (but not the binarized model).

Obviously, one could make other assumptions about those relative frequencies and gain somewhat different answer. Using the full model one could also get a more nuanced answer about which levels we are reasonably confident are associated with a different outcome. 

If you cannot convince your collaborator to be more interested in the more nuanced answer, you should at least be able to force them to explicitly state their assumptions about the relative frequencies and answer that variant of the binary question.

**Monotonic predictors**

As noted by Frank, the `brms` package uses a neat construction to build monotonic but highly flexible predictors - details are described in [Bürkner & Charpentier 2020](https://doi.org/10.1111/bmsp.12195). Although I am a fan of the Bayesian approach, there is IMHO no fundamental reason why such a construction wouldn't work in a frequentist maximum likelihood setting - I just haven't seen it done. Indeed the paper cites a couple approaches like using penalized monotonic splines (on the category index) that definitely work in the freuqentist setting, see e.g. [de Leeuw: Computing and Fitting Monotone Splines](https://rpubs.com/deleeuw/268327), or [`gamlss::pbm()`](https://search.r-project.org/CRAN/refmans/gamlss/html/ps.html) - although those require fixing the direction of the effect beforehand.