More than likely, if you're writing a paper, you're well on your way to what you need for practical significance. You've reviewed the literature and studied the subject matter and people have said, either explicitly or implicitly, what a practically significant amount is. All you need to do is cite that literature and use it in your article. If you want something just on the utility of effect sizes just google search effect size and your field (and perhaps cohen, 1962). Often there are effect size promotion papers specific to various disciplines. You could also look at Cohen 1962 as an example of how this kind of problem is approached (but not an example of what effect size is practically significant in your case-- it's unfortunate typical use).

There is no odds ratio that's a gold standard but I'm a bit surprised you're having a hard to explaining one. I suppose it's not surprising giving most textbook treatments. Betting uses odds ratios all of the time and people are familiar with it. If you need to explain it use that analogy. The odds of "Come by Chance" winning the race is 3:1 (or 3). Most people know what that means in payout. And they can see it's the number of times you'd win the race against the number of times you'd lose.

Of course, if this isn't just some proportion but something like a diagnostic odds ratio it's probably best to also be expressing things in specificity and sensitivity as well. Odds ratios alone miss bias in that case (and other similar ones where it is important).

More than likely, if you're writing a paper, you're well on your way to what you need for practical significance. You've reviewed the literature and studied the subject matter and people have said, either explicitly or implicitly, what a practically significant amount is. All you need to do is cite that literature and use it in your article. If you want something just on the utility of effect sizes just google search effect size and your field (and perhaps cohen, 1962). Often there are effect size promotion papers specific to various disciplines. You could also look at Cohen 1962 as an example of how this kind of problem is approached (but not an example of what effect size is practically significant in your case-- it's unfortunate typical use).

There is no odds ratio that's a gold standard but I'm a bit surprised you're having a hard to explaining one. I suppose it's not surprising giving most textbook treatments. Betting uses odds ratios all of the time and people are familiar with it. If you need to explain it use that analogy. The odds of "Come by Chance" winning the race is 3:1 (or 3). Most people know what that means in payout. And they can see it's the number of times the horse is expected to lose the race (3) against the number of times it would win (1), or 75% of the time.

Of course, if this isn't just some proportion but something like a diagnostic odds ratio it's probably best to also be expressing things in specificity and sensitivity as well. Odds ratios alone miss bias in that case (and other similar ones where it is important).