Where should folks turn if they want mental health support for depression *right now* and aren't in crisis?

Traditional talk therapy often has long waitlists

The therapy apps you've heard about promising quick access to treatment have lots of problems

What I recommend 🧵
Adults Part I

Program: Deprexis
Content: 10 self-guided, internet-based modules (most grounded in evidence-based approaches)
Cost: ~1-2 sessions of therapy ($280)
Evidence: Solid meta-analytic evidence across >10 RCTs journals.plos.org/plosone/articl…
Link: orexo-store-2.mybigcommerce.com
Adults Part II

Program: MoodGYM
Content: 5 self-guided, internet-based modules (all grounded in CBT-based approaches)
Cost: <1 session of therapy ($27)
Evidence: Somewhat shaky meta-analytic evidence across >10 RCTs researchgate.net/profile/Conal-…
Link: moodgym.com.au
Adolescents Part I

Program: Project YES
Content: 3 single session intervention modules (All grounded in evidence-based strategies)
Cost: Free
Evidence: Both modules tested reduced depression vs. placebo in ~2,500 person RCT psyarxiv.com/ved4p/
Link: schleiderlab.org/yes.html
Adolescents Part I Contd

More evidence: mental.jmir.org/2020/6/e20513/
Languages: English and Spanish
Conflict of Interest: I'm part of the research team that developed the Project YES interventions/have access to the anonymous data/think the folks on the team are great
Everyone Part I

Program: One Mind PysberGuide
Content: Reviews of most avaialbe mental health support apps (If you want a non Deprexis/MoodGYM/Project YES self-guided option)
Cost: Free
Link: onemindpsyberguide.org
Shout out to: @steveschueller
Everyone Part II

Program: @therapy4theppl
Content: Directory of free and low cost therapy (including research studies that might be able to enroll you more quickly than other kinds of treatment)
Cost: Free
Link: therapy4thepeople.org
Shout out to: @ashleyscastro
I hope these resources are useful for at least some folks!

I'm very pro folks getting talk therapy, and most folks have to wait a long time before they can get that kind of help

I hope these alternatives can at the very least be useful to folks while they wait

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More from @mcmullarkey

29 Sep
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

tandfonline.com/doi/full/10.10…

journals.sagepub.com/doi/full/10.11…
There's evidence supervisors and trainees care more about a supervisors' "relational characteristics" than their "transmission of clinical know how"

It's ok to want to spend time with people we like, and there's no guarantee that will help patients

ncbi.nlm.nih.gov/pmc/articles/P…
Read 14 tweets
19 May
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)

swirlstats.com/students.html
R, Part II

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

r4ds.had.co.nz
Read 14 tweets
28 Feb
I just found out a paper we first submitted ~3 years ago was accepted! We used an N > 1,000 sample, open data/code, and robust methods

I'm proud of this paper, and it also helped radicalize me against a lot of the stories we tell ourselves about peer review

A 🧵
The many reviews we received were almost uniformly hostile, confused, non-constructive, or some combination
The paper definitely got better throughout the process, and that had ~0 to do with the reviews

Real reason #1: A wonderful, ongoing collaboration with a stellar biostatistician/many other great collaborators

Real reason #2: I got better at coding/new tools became available
Read 22 tweets
5 Sep 19
Trying to balance:
- Having genuine empathy for people who are staring down the barrel of their life's work not replicating
- Not reinforcing power structures and practices that led to a world where those barrels are all too common
Hearing @minzlicht talk about this on the "Replication Crisis Gets Personal" @fourbeerspod episode brought home to me how lucky I am to be early in my career now as opposed to 20 or even 10 years ago
But his example* reminds me people in power have a choice when confronted with a much messier literature than initially described

They can double down, or they can engage meaningfully with a more complicated world

*And many others, my mentions aren't ever comprehensive!
Read 12 tweets
24 May 19
About to live tweet "Recent Advances in the Use of Modeling to Explain and Predict Psychological Phenomena From Nomothetic & Idiographic Perspectives" with @EikoFried @talyarkoni @DepressionLab @aaronjfisher #aps19dc

It's already won the award for longest title, so good start!
@EikoFried @talyarkoni @DepressionLab @aaronjfisher Twitter-less (I think!) Don Robinaugh and Jonas Dalege are also presenting
@EikoFried @talyarkoni @DepressionLab @aaronjfisher .@EikoFried starts us off by reminding us that psychological modeling are complex, multicausal constructs and our approaches to these constructs often don't match that complexity
Read 61 tweets
6 Feb 19
New preprint from @JSchleiderPhD & me: Emotion and anxiety mindsets share little unique variance with internalizing problems in adults once you account for hopelessness (Ns = 200, 430)

Open code & data + interpretations in this thread!

psyarxiv.com/qtrxs/
We used commonality analysis (CA), which allows us to directly examine how much predictive variance is unique & shared among predictors

This technique can help us identify important individual predictors even when they're highly correlated (A no-no in traditional linear models)
If you want to try it out for yourself, the code and the data are part of this OSF project!

osf.io/wrc2m/

You can also apply the code to your own data. Would be great to see more CA papers out there given how often relevant predictors are highly correlated
Read 11 tweets

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