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Showing posts with the label mental health

Using data about antidepressant efficacy to illustrate Cohen's d, demonstrate why you need a control group, talk about interactions.

This example is from The Economist and behind a paywall. However, it is worth using one of your free monthly views to see these visualizations of how much improvement Ps experience. That said, whenever I talk about antidepressants in class, I remind my students MANY TIMES that I'm not that kind of psychologist, and even if I was, I'm not their psychologist. Instead, they should direct any and all medication questions to their own psychologist. This blog post was inspired by " Antidepressants are over-prescribed, but genuinely help some patients " from The Economist, which was in turn inspired by  " Response to acute monotherapy for major depressive disorder in randomized, placebo-controlled trials submitted to the US FDA: individual participant data analysis", by M.B. Stone et al., BMJ, 2022; "Selective publication of antidepressant trials and its influence on apparent efficacy: updated comparisons and meta-analyses of newer versus older trial s", ...

Suicide hotline efficacy data: Assessment, descriptive data, t-tests, correlation, regression examples abound

ASIDE: THIS IS MY 500th POST. PLEASE CLAP. Efficacy data about a mental health intervention? Yes, please. The example has so much potential in a psych stats classroom. Or an abnormal/clinical classroom, or research methods. Maybe even human factors, because three numbers are easy to remember than 10? This post was inspired by an NPR story  by Rhitu Chatterjee. It is all about America's mental health emergency hotline's switch from a 10-digit phone number to the much easier-to-remember three digits (988), and the various ways that the government has measured the success of this change. How to use this (and related material) in class: 1) Assessment. In the NPR interview, the describe how several markers have improved: Wait times, dropped calls, etc.  Okay, so the NPR story sent me down a rabbit hole of looking for this data so we can use it in class. Here is the federal government's website about  988  and a link to their specific  988  performance data,...

Descriptive data example (and more) from Crisis Text Hotline

This blog post was inspired by the brilliant Leslie Berntsen. Her ACT 2021 presentation,  " I'm not a therapist: Mental health education and advocacy for non-clinicians," was about teaching and sharing sound practices in mental health care when you, yourself, are not a mental-health-care-type-psychologist.  Anyway, she had this really great idea about sharing user/trends/etc. data from the Crisis Text Line  (CTL) with students. CTL is a text-based mental health crisis hotline. It is staffed 24/7. I include it on my syllabus as a resource for my struggling students because, unlike my uni's very hard-working counseling center, CTL is always available.  https://www.crisistextline.org/ Why share data from CTL in your stats class? Because you can use it to a) teach a bit of stats, b) introduce students who need this resource to the resource, c) introduce potential volunteers to the service. Here are my ideas for how you could use this data in class. 1. Data visuali...

Harris' "Reviews Of Medical Studies May Be Tainted By Funders' Influence"

This NPR story is a summary of the decisively titled " The Mass Production of Redundant, Misleading, and Conflicted Systematic Reviews and Meta-analyses " authored by Dr. John Ioannidis. The NPR story provides a very brief explanation of meta-analysis and systematic reviews. It explains that they were originally used as a way to make sense of many conflicting research findings coming from a variety of different researchers. But these very influential publications are now being sponsored and possibly influenced by Big Pharma. This example explains conflicts of interest and how they can influence research outcomes. In addition to financial relationships, the author also cites ideological allegiances as a source of bias in meta-analysis. In addition to Dr. Ioannidis, Dr. Peter Kramer was interviewed. He is a psychiatrist who defends the efficacy of antidepressants. He suggests that researchers who believe that placebos are just as effective as anti-depressants tend to analy...