You're spot on that significant ≠ meaningful effect. But I'd push back slightly on the example. A very low p-value doesn't always imply a meaningful effect, but it's not independent of effect size either. A p-value comes from a test statistic that's basically:
(effect size) / (noise / sqrt(n))
Note that bigger test statistic means smaller p-value.
So very low p-values usually come from bigger effects or from very large sample sizes (n). That's why you can technically get p<0.001 with a microscopic effect, but only if you have astronomical sample sizes. In most empirical studies, though, p<0.001 does suggest the effect is going to be large because there are practical limits on the sample size.
(effect size) / (noise / sqrt(n))
Note that bigger test statistic means smaller p-value.
So very low p-values usually come from bigger effects or from very large sample sizes (n). That's why you can technically get p<0.001 with a microscopic effect, but only if you have astronomical sample sizes. In most empirical studies, though, p<0.001 does suggest the effect is going to be large because there are practical limits on the sample size.