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The Tyranny of Involuntary Psychiatric Treatment

The Tyranny of Involuntary Psychiatric Treatment
counterpunch.org - get the latest breaking news, showbiz & celebrity photos, sport news & rumours, viral videos and top stories from counterpunch.org Daily Mail and Mail on Sunday newspapers.

New York , United States , San Francisco , Hannah Arendt , Wole Soyinka , Arlie Hochschild , Richard Lewontin , Bruce Levine , Martin Luther King , Steven Rose , Barbara Ehrenreich , Julius Hoffman , Jay Katz , Gavin Newsom , Albert Solnit , Los Angeles , Joel Alden Schlosser , Joseph Goldstein , Paul Robeson , Robert Furman , Bob Dylan , Eric Adams , Adolf Eichmann , C Wright Mills , Bobby Seale , Roberta Furman ,

CNN Fareed Zakaria GPS June 4, 2024 14:54:00

The region going back as far as 1850. as the new york times notes, they use 21 models to simulate what the weather would be like if humans had never pumped greenhouse gases into the atmosphere. they found that the heat wave in the pacific northwest would have been virtually impossible without the advent of climate change. but there was something else that stumped the scientists. the heat wave was so far beyond historically observed temperatures that it couldn t be captured by the statistical model. calculating at current levels of global warming, the model notes that something strange like this could happen once every thousand years. perhaps the weather last month was a once in a thousand-year ....

New York Times , Greenhouse Gases , Heat Wave , Something Else , Climate Change , Couldn T , Pacific Northwest , Statistical Model , A Thousand ,

578,555 people have died from COVID-19 in the US, or maybe it's 912,345 – here's why it's hard to count


When the Institute for Health Metrics and Evaluation at the University of Washington released its estimate that COVID-19 had killed 912,345 people in the U.S. by May 6, 2021, many were shocked. That’s 60% higher than the 578,555 coronavirus-related deaths officially reported to the U.S. Centers for Disease Control and Prevention over this same period.
How can two estimates differ so widely? It’s not like the Institute for Health Metrics and Evaluation researchers stumbled upon a morgue of more than 300,000 dead people who hadn’t been tracked elsewhere.
Here’s what goes into some of the various counts of COVID-19 pandemic deaths and how I as a statistician think about their differences. ....

National Vital Statistics System , Ben Hasty Medianews Group Reading Eagle , Us Centers For Disease , University Of Washington , World Health Organization , Institute For Health Metrics , Health Metrics , Disease Control , Reading Eagle , Covid 19 , Ovid 19 Pandemic , Death Certificate , Excess Deaths , Statistical Model , தேசிய இன்றியமையாதது புள்ளிவிவரங்கள் அமைப்பு , எங்களுக்கு மையங்கள் க்கு நோய் , பல்கலைக்கழகம் ஆஃப் வாஷிங்டன் , உலகம் ஆரோக்கியம் ஆர்கநைஸேஶந் , நிறுவனம் க்கு ஆரோக்கியம் அளவீடுகள் , ஆரோக்கியம் அளவீடுகள் , நோய் கட்டுப்பாடு , ரீடிஂக் கழுகு , விட் சர்வதேச பரவல் ,

"Application of mixture distributions for identifying thresholds of fre" by Bianca Suesse, Luise Lago et al.


Abstract
Background: There is a need for greater understanding about frequent and high use of inpatient mental health services, and those with ongoing increased needs. Most studies employ a threshold of frequent use (e.g. numbers of admissions) and high use (e.g. lengthy stays) without justification. Aims: To identify model-driven thresholds for frequent/high inpatient mental health service use and contrast characteristics of patients identified using various models and thresholds. Method: Retrospective population-based study using 12 years of longitudinal data for 5631 patients admitted with a mental health diagnosis. Two-component negative binomial and poisson mixture (truncated/untruncated) models identified thresholds for frequent/high use in a 12-month period. Results: The two-component negative binomial mixture model resulted in the best model fit. Using negative binomial-derived thresholds, 5.3% of patients had a period of frequent use (admitted six or more times), 15.8 ....

Mental Health , Statistical Model ,