That's an interesting question. Sometimes we face such a situation that a one-factor solution doesn't fit the data well, and yet the best fitting solution suggests the presence of a strong general factor. In practical terms, factor correlations higher than .70 could be considered a sign of unidimensionality. I believe it could be very useful to examine the direct effect of both specific factors and a general factor on your indicators (i.e. bifactor modeling) and analyze how important such general factor is (omegaH and ECV are useful here).
That's an interesting question. Sometimes we face such a situation that a one-factor solution doesn't fit the data well, and yet the best fitting solution suggests the presence of a strong general factor. In practical terms, factor correlations higher than .70 could be considered a sign of unidimensionality. I believe it could be very useful to examine the direct effect of both specific factors and a general factor on your indicators (i.e. bifactor modeling) and analyze how important such general factor is (omegaH and ECV are useful here).
I would recommend running a Principal Component Analysis on your "factors", looking at the loading on the first component, removing any "factors" with a loading less than 0.4 and re-running if necessary, then saving the regression model scores as this will be more accurate than adding your "factors" together anyway. In this way you do not need to worry about the question you have asked.
I have put your word factor in quotes because we normally start with variables then summarise them into factors, but if you have already done this then your question is well posed.
Please see my study guide on scale reliability for more information.