When it comes to studies, bias indeed creates huge challenges when you want to determine numbers. How many donor conceived are interested in contact with the donor is one of such challenging answers. Yet, those that are interested will benefit from the option to contact and those that are not interested can simply choose to do nothing. Also, it's clear from the development of the commercial DNA databases that more donor conceived are finding out. So when it comes to policies, logical thinking may be more important than scientific studies.
Yes, policy and rule making related to donor conception cannot wait for “gold standard” empirical evidence. In addition to evidence derived from practice and lived experience, we can also use the "mechanistic evidence" you described. We can break down the issue to its underlying processes and causal mechanisms to create policies that support the range of needs expressed by DCP. We also regularly draw from "parallel evidence" from comparable scenarios like adoption.
When it comes to studies, bias indeed creates huge challenges when you want to determine numbers. How many donor conceived are interested in contact with the donor is one of such challenging answers. Yet, those that are interested will benefit from the option to contact and those that are not interested can simply choose to do nothing. Also, it's clear from the development of the commercial DNA databases that more donor conceived are finding out. So when it comes to policies, logical thinking may be more important than scientific studies.
Yes, policy and rule making related to donor conception cannot wait for “gold standard” empirical evidence. In addition to evidence derived from practice and lived experience, we can also use the "mechanistic evidence" you described. We can break down the issue to its underlying processes and causal mechanisms to create policies that support the range of needs expressed by DCP. We also regularly draw from "parallel evidence" from comparable scenarios like adoption.