In this instance you have specifically chosen probabilities p=0.001 that are extreme compared to sample size 100. The expected number of succeses is ceiling(99.9)=100. When approximating a binomial the poisson dist. is better for "very small" p and the normal will work best for p around 0.5. The way I see it you are choosing p=0.001 thereby creating a problem for the normal distribution approximation that more relates to the problem of approximating a bounded distribution taking values in 0,1,..,100 with an unbounded -inf,inf than it relates to the problem of approximating something discrete with a continuous distribution. The continuity correction does not solve the first type of these problems and the fact that the problem exists is no news.

So to answer the question I guess you could do a simulation study on samples where you in the first place actually would use a normal distribution or where the problem of approximation is a problem relating to what the correction is intended to correct.

As an alternative you could simple invent the Trinker continuity approximation where in the case you get a result below 0 you round up to zero and in the case you get a value above 1 you round down