9 Comments

You seem to discount American exceptionalism as an underlying cause. (I kid, I kid.)

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Speaking of weed, how about an episode on the effect of weed on young adult brain ... asking for a friend who is a parent of a 19-year-old 😝

Regarding the episode itself, this is what respectful and vigorous scientific debate looks like. Thank you for modeling it.

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This was an especially valuable episode--thank you, gents! The phrase "mental health unawareness" made me chuckle a bit as it rings true from my vantage point. I'm a high school special education teacher in the states with 23 years experience ("oldster" I think is the official term), and I can’t tell you what a turnaround we’ve experienced in public schools since I started--even from 5-10 years ago. My field especially has experienced a substantial increase in kids placed on IEPs & 504 plans for social-emotional disabilities, which, quite frankly, has been encouraged by doctors and outside providers. Some kids are genuinely suffering and need specialized instruction, but many do not. In a culture saturated with mental health crisis messaging, this is very hard for concerned parents to make heads or tails of. Most educators genuinely want to help, but are increasingly uncomfortable with the mental health practitioner-by-proxy role we find ourselves in.

On phone use, I’m also skeptical it’s the driving force behind elevated mental illness rates. I've read much of Jean Twenge's work (as well as Jonathan Haidt's) and it’s interesting, but questions linger for me. I’m curious if and how they accounted for the mental health infrastructure that’s built into public institutions in their findings. By that I mean the standard professional protocol of asking if one feels “safe” at home, whether they have a trusted adult/friend, do they have thoughts of harm, etc. If a child is in distress at school and visits his/her guidance counselor, these questions will be asked. I imagine rates of heart disease went up after doctors started taking everyone’s blood pressure. Wouldn’t ubiquitous mental health screens have a similar effect?

For a more measured, optimistic view of screens and kids, I found Vassar psychologist Dr. Abigail Baird’s research on the adolescent brain fascinating, as well as Berkeley professor Dr. Alison Gopnik’s evolutionary perspective. They brought me back down from the ledge. :)

Apologies for the long comment. Wonderful episode!

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Great episode! I'd love to hear more about your thoughts on Haidt's other work. I suspect you could make several episodes about The Coddling of the American Mind!

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I will admit on this one that my prejudice/Bayesian prior [delete as appropriate] on this one was to be minded to agree with the screentime hypothesis. I came away from this episode more doubtful about the solidity of the evidence and would agree that it's not strong enough to do something drastic like ban phones for the under 16s. That said, I'm not sure it steel-mans Haidt's argument as, IIRC, Haidt doesn't actually go so far as to propose that - I believe he's a fan of giving children more independence and 'wait until [Year] 8' for giving them a phone.

As a parent whose 9yo has friends starting to get phones I'm left wondering whether the precautionary principle should apply (i.e. the link hasn't been comprehensively disproven) and that the 'wait a few years' approach isn't a reasonable one. Arrrgh, why can't science be simple.

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Feb 3·edited Feb 3

Just around 55 mins; the phones vs injectable drugs comparison made me pause + dig out the cited papers:

Orben et al 2019. https://www.nature.com/articles/s41562-018-0506-1

Twenge et al 2022. https://www.sciencedirect.com/science/article/pii/S0001691822000270

Twenge plots lots of 'betas' on one chart, which makes _no_ sense because the betas (regressions slopes) for different things they plot have different units. Orben makes it clear that they are talking about standardised betas, a.k.a. regression coefficients, so I assume Twenge is too.

Anyway, if you are talking about regression coefficients you would absolutely expect the effect size for phones to be larger than the one for heroin. And the reason is that everyone uses phones and hardly anyone uses heroin. Variance explained is just the square of the correlation,* so again you would expect variance explained by heroin to be smaller than variance explained by phones.

What's going on here is that there's confusion between a) the effect size for an _individual_ who uses phones/heroin, where you would for sure expect heroin to have a massively larger effect, and b) the effect size in the _population_, which is affected by the numbers of people who use phone/heroin. Standardised betas/correlation are a measure of b).

*And is a really problematic concept, but that's another story...

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