"Usefulness" certainly isn't the orthodox Bayesian phrasing. I call myself a Bayesian because I recognize that Bayes's Rule is the right thing to use in these situations. Whether or not the probabilities assigned to hypotheses "actually are" probabilities (whatever that means), they should obey the same mathematical rules of calculation as probabilities.

But precisely because only the manipulation rules matter, I'm not sure it is worth emphasizing that "to be a good Bayesian" you must accord these probabilities the same status as other probabilities. A hardcore Frequentist is not going to be comfortable doing that. Heck, I'm not sure I'm comfortable doing that. Data and event probabilities are things that can eventually be "resolved" to true or false, by looking after the fact. Probability as plausibility makes sense for these things.

But for hypotheses and models, I ask myself "plausibility of what? Being true?" Almost certainly, the "real" model (when that even makes sense) isn't in our space of models. For example, a common, almost necessary, assumption is exchangeability: that any given permutation of the data is equally likely -- effectively that all data points are drawn from the same distribution. Data often doesn't behave like that, instead having a time drift. Coins being tossed develop wear, cards being shuffled and dealt get bent.

I really do prefer to think of some models being more or less useful. Of course, following this path shades into decision theory: we might want to assign priors according to how "tractable" the models are, including both in specification (stupid models that just specify what the data will be take lots of specification, so should have lower initial probabilities). Models that take longer to compute data probabilities should similarly have a probability penalty, not simply because they're implausible, but because we don't want to use them unless the data force us to.