A close friend tells you they're having a hard time getting out of bed every day and feeling really down
They get a new Not-Theranos blood test that "detects depression" and test negative
Do you believe your friend or the blood test?
This dynamic is what makes most mental health diagnoses simultaneously much trickier and much easier than most physical health diagnoses
Even if we don't expect perfect insight, how people feel about their lives often matters more than any "objective" test
There are real critiques of self report measures, and I've written some of them!
But the outcome data evaluated by Big Tech in the original WSJ article wasn't weak because it was self-reported. It was weak because of mismatches between the research design and the conclusions
I will also note that some predictors of interest in this space like screen time aren't well assessed by self-report *at all*
I'm talking about not underrating validated outcome metrics of well-being, depression symptoms, etc.
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If you ever want to sound like an expert without paying attention, you only need two words in response to any question
"It depends"
A thread on why we should retire that two word answer 🧵
When people say "it depends" they often mean the effect of one variable depends on the level of at least one other variable
For example:
You: Does this program improve depression?
Me, Fancy Expert: Well, it depends, probably on how depressed people were before the program
Understandably you'll want some evidence for my "it depends"
Luckily my underpaid RA has already fired up an ANOVA or regression, and *I* found that how depressed folks were before the program moderated the effect of the program
And especially if you have a psych background, you might think we *need* an experiment to understand causes
While I love experiments, here's a thread of resources on why they're neither necessary nor sufficient to determine causes 🧵
This paper led by @MP_Grosz is a great start! It persuaded me that merely adjusting our language (eg saying "age is positively associated with happiness" instead of "happiness increases with age") isn't enough
If we prioritized improving patients' and trainees' lives clinical psych's structures would look entirely different
A part touched on but (understandably!) not emphasized in this piece: There's vanishingly little evidence our training improves clinical outcomes for patients
🧵
Multiple studies with thousands of patients (though only 23-39 supervisors each!) show that supervisors share less than 1% of the variance in patient outcome
And that's just correlation, the causal estimate could be much smaller
Still responding to folks re: my transition to data science post! I'll get to everyone, promise!
Given the interest I thought people might want to know the (almost all free/low cost!) resources I used to train myself for a data science role
A (hopefully helpful) 🧵
R, Part I
My first real #rstats learning experience was using swirl. I loved that I could use it inside of R (rather than having to go back and forth between the resource and the RStudio console)
A cliche rec, but it's cliche for a reason. R for Data Science by @hadleywickham & @StatGarrett transitioned me from "kind of messing around" to "wow, I did that cool thing" in R. It's absolutely a steal that it's available for free