B. Transvestism is often accompanied by AGP.
C. The presence of AGP in transvestism increases the likelihood of [developing] Gender Dysphoria.
1/
It’s often claimed that “The Concept of Autogynephilia and the Typology of Male Gender Dysphoria” by @BlanchardPhD has been debunked.
I thought it would be interesting to see, at a basic level, how the concept and typology compares to the DSM-5.
A. The definition of AGP.
B. Transvestism is often accompanied by AGP.
C. The presence of AGP in transvestism increases the likelihood of [developing] Gender Dysphoria.
E. Natal males with early-onset GD are almost always androphilic. The majority of natal males with late-onset GD are gynephilic; additionally, they frequently [have histories of] engaging in transvestic behavior with sexual excitement.
F. In many cases of natal males with late-onset GD, transvestic behavior with sexual excitement is a precursor to their GD.
Observation 1:
There appears to be a strong correlation between the “early-onset GD” group in the DSM-5 and the “homosexual transsexual” (HSTS) group in the Blanchard typology.
Observation 2:
There appears to be a strong correlation between the “late-onset GD” group in the DSM-5 and the “nonhomosexual transsexual” (autogynephilic/AGP) group in the Blanchard typology.
Observation 3:
The DSM-5 categorizes its GD groups based on age of onset, while the Blanchard typology categorizes its groups
based on sexual orientation.
Although this creates room for incongruence between the DSM-5 groups and the Blanchard typology groups, it also demonstrates a strong resemblance, given that the DSM-5 indicates such a strong relationship between age of GD onset and sexual orientation.
More from Society
I've seen many news articles cite that "the UK variant could be the dominant strain by March". This is emphasized by @CDCDirector.
While this will likely to be the case, this should not be an automatic cause for concern. Cases could still remain contained.
Here's how: 🧵
One of @CDCgov's own models has tracked the true decline in cases quite accurately thus far.
Their projection shows that the B.1.1.7 variant will become the dominant variant in March. But interestingly... there's no fourth wave. Cases simply level out:
https://t.co/tDce0MwO61
Just because a variant becomes the dominant strain does not automatically mean we will see a repeat of Fall 2020.
Let's look at UK and South Africa, where cases have been falling for the past month, in unison with the US (albeit with tougher restrictions):
Furthermore, the claim that the "variant is doubling every 10 days" is false. It's the *proportion of the variant* that is doubling every 10 days.
If overall prevalence drops during the studied time period, the true doubling time of the variant is actually much longer 10 days.
Simple example:
Day 0: 10 variant / 100 cases -> 10% variant
Day 10: 15 variant / 75 cases -> 20% variant
Day 20: 20 variant / 50 cases -> 40% variant
1) Proportion of variant doubles every 10 days
2) Doubling time of variant is actually 20 days
3) Total cases still drop by 50%
While this will likely to be the case, this should not be an automatic cause for concern. Cases could still remain contained.
Here's how: 🧵
One of @CDCgov's own models has tracked the true decline in cases quite accurately thus far.
Their projection shows that the B.1.1.7 variant will become the dominant variant in March. But interestingly... there's no fourth wave. Cases simply level out:
https://t.co/tDce0MwO61
Just because a variant becomes the dominant strain does not automatically mean we will see a repeat of Fall 2020.
Let's look at UK and South Africa, where cases have been falling for the past month, in unison with the US (albeit with tougher restrictions):
Furthermore, the claim that the "variant is doubling every 10 days" is false. It's the *proportion of the variant* that is doubling every 10 days.
If overall prevalence drops during the studied time period, the true doubling time of the variant is actually much longer 10 days.
Simple example:
Day 0: 10 variant / 100 cases -> 10% variant
Day 10: 15 variant / 75 cases -> 20% variant
Day 20: 20 variant / 50 cases -> 40% variant
1) Proportion of variant doubles every 10 days
2) Doubling time of variant is actually 20 days
3) Total cases still drop by 50%
