C. Thi Nguyen

So the, oh God, how did, how did, how did we get, oh, the cave, cave.

I need to go back to the cave.

Okay, let me go back to the cave.

So here's the worry.

There are some things that is very, that data is a very appropriate method for, something that's hyper context invariant, something that everyone can pick up on easily.

There are other things that are subtle and rich and important that are highly contextually variable and highly dependent on interaction with a particular personality or a particular specialization.

Metrics won't be good at picking up on that.

The cave for me is when we start treating as reality only the things that we have metrics and data for, treating those as the only real things.

And then the rest of the stuff falls.

And I'll just, so in the university for me, it's like people will target, it's very easy to target student success and graduation rates.

And if you want to say something like, but what about making students who are ethical and thoughtful, right?

And you say, I want to do an intervention that might slow down, slow things down a tiny bit, but it'll make students more ethical and thoughtful.

Since I don't have,

clear metrics for that, that like falls off the radar.

Here's something I think I've been thinking about a lot that I think is, that might be controversial to some people.

I think when making public health decisions, we often hyper-target things that involve mortality rates and we don't hyper-target things that involve maintaining communities or traditions.

It's much, it's much, I mean, I'm thinking here about COVID responses.

And I think like there was a period where we were in that debate where,

Mortality was the only thing that mattered and like mental health and communities did not matter.

And I think part of the reason was that one of these things had a very kind of clear reality in the world of easy accountability.