How to compare rows within a dataframe column and check if value is changing?

I have a data frame as seen:

How to check conditions when results change from ‘NO’ to ‘OK’ and if it does populate the next column as true. Results should look like this:

>Solution :

My understanding question is need test OK if duplicated group only, so here are solutions with more data:

#test OK
m = df['Result'].eq('OK')
#first values per groups by consecutive values
m1 = m.ne(m.shift())
#chained consecutive OK with first values per groups
df['check1'] = m1.cumsum().duplicated(keep=False) & m1

#chained OK if from NO to OK with consecutive OK
df['check2'] = (m & df['Result'].shift().eq('NO')) & m1.cumsum().duplicated(keep=False)
print (df)
   Result  check1  check2
0      OK   False   False
1      NO   False   False
2      OK    True    True
3      OK   False   False
4      NO   False   False
5      OK    True    True
6      OK   False   False
7      OK   False   False
8      NO   False   False
9      OK   False   False
10     NO   False   False
11     OK   False   False