Mastodawn

RE: https://flipboard.com/@npr/politics-v8m4p3ukz/-/a-lyLNjQfRQJKEoDaSRVJTpw%3Aa%3A3195441-%2F0

Do U.S. voters realize the scale and cost of the 47th #POTUS's *drastic* #immigration "slow-walking"?

This backlog is bigger than ALL FOUR YEARS of the 45th POTUS's term.

It's stopped TONS of #science by removing work authorization from LOADS of #scientists who were ALREADY LEGALLY WORKING HERE FOR YEARS (e.g., when graduating Ph.D. #students or #postdocs have to transition to an H-1B #visa when OPT expires).

We had to halt multiple #decisionScience projects when a Research Project Manager completed their doctorate. Why? Because #USCIS simply isn't processing certain #OPT applications — and #H1B processing much slow than normal times. In the past year, the #UnitedStates multiplied this loss of #innovation and #productivity by HUNDREDS OF THOUSANDS! And with no detectable benefit to #security, etc.

Naturally, the highest-skilled foreign nationals are taking their skills to other countries, contributing to further #brainDrain from the #USA.

Incredible waste and inefficiency.

Runtime Arguments Mar 30

Episode 24 of our technology podcast @RuntimeArguments (http://RuntimeArguments.fm). This episode is called "Bayes' Rule - The Formula For Learning Everything".

This is one of Wolf's @YesJustWolf favorite things to talk about and might be the most important thing you've never heard of. Jim's @jammcq heard about it so often he's starting to be numb, but in this episode Wolf's goal was to put Bayes into Jim's brain the same way that Vim is in Jim's fingers!

As always, we want to know what you think:

[email protected]

https://www.buzzsprout.com/admin/2469780/episodes/18877456-24-bayes-rule-the-formula-for-learning-everything

#Technology #Podcast #Bayes #AF447 #TomW #Kahneman #Hofstadter #Shannon #DecisionScience #Laplace #GodelEscherBach

Runtime Arguments

Conversations about technology between two friends who disagree on plenty, and agree on plenty more.

Buzzsprout

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Zajey Feb 11

Yeah Y'all know what this is.

#Zajey #Science #Research #ScientificMethod #SystemsThinking #Causality #CausalInference #DecisionScience #ComplexityScience #ComplexSystems #NonlinearDynamics #ChaosTheory #Probability #Statistics #Uncertainty #RiskAnalysis #Modeling #Inference
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Nick Byrd, Ph.D.Feb 10

How else can automated text messages educate medical patients?

Adding automated text messages to a paper-based decision aid program improved lung #cancer screening knowledge and screening rates.

https://doi.org/10.2196/69044

#medicine #edu #decisionScience #healthScreening #oncology

Previsit Preparation for Shared Decision-Making in Lung Cancer Screening in Primary Care Using a Paper Decision Aid and an Automated Text Messaging Program: Quasi-Experimental Pilot Study

Background: Patient-provider discussions and shared decision-making (SDM) are essential for tailoring lung cancer screening (LCS) decisions to individual patients. However, implementation of SDM in primary care settings is challenging. Innovative approaches are needed to reach patients eligible for LCS and help them prepare for LCS discussions in primary care settings and to increase the uptake of LCS. Objective: We piloted pre-visit preparation comparing two strategies: a paper decision aid (DA) (DA group), and an enhanced comparator strategy consisting of the paper DA plus an automated text message program (DA+TM group) designed to promote patient-provider LCS discussions. We explored feasibility and gathered preliminary data on its potential effects on LCS discussions, decision-making, and LCS uptake in primary care settings. Methods: In a sequential quasi-experimental pilot, we recruited patients who were eligible for LCS in a single academic healthcare system. Prior to an upcoming visit, participants in both groups received a paper-based DA by mail. In the DA+TM group, participants also received a series of automated text messages to help them prepare for their LCS discussions. We monitored participant recruitment and retention, and patient engagement in DA and text messages. In exploratory analyses, we assessed patient-provider discussion of LCS, SDM, patient knowledge, decision conflict at baseline and in follow-up telephone surveys, and LCS completion measured by electronic health records. Results: We enrolled 49 participants (DA group = 19, DA+TM group = 30). Participants were predominantly White, with a median age of 61.0 (IQR, 57.0-65.0), and 58.3% were female. Engagement in both groups was high. LCS knowledge significantly improved in the DA+TM group (4.5 baseline vs. 6.0 follow-up; P=.003), versus no change in the DA group (5.0 baseline vs. 5.0 follow-up, P=.23). Median LCS knowledge change from baseline to follow-up was 0.5 (IQR -1.0-2.5) in the DA group, and 1.5 (IQR 0-3.0) in the DA+TM group (P=.24). Decision conflict in both groups significantly decreased (DA group: 37.5 baseline vs. 0 follow-up, P<.001; DA+TM group: 50.0 baseline vs. 20.0 follow-up, P=.003). The median SDM process score (a measure of SDM) was 3.0 in the DA group and 2.0 in the DA+TM group (P=.11). The LCS completion rates were 5.3% in the DA group and 31.0% in the DA+TM group at 3 months (P=.07), and 26.3% in the DA group, and 34.5% in the DA+TM group at 6 months (P=.75). Conclusions: We showed that pre-visit preparation was feasible in primary care settings. The enhanced strategy utilizing text messaging not only reduced decisional conflict but also improved LCS knowledge. An enhanced, text message-based strategy has the potential to reach and engage broader LCS-eligible populations and prepare patients for LCS discussions with their primary care providers, which may ultimately improve informed decision-making and LCS uptake.

JMIR Formative Research

Nick Byrd, Ph.D.Feb 9

RE: https://mastodon.online/@tomstafford/116041517300854913

As I explain in my post about the #BiasFallacy (https://byrdnick.com/archives/11072/the-bias-fallacy), Tom shows how #bias can be a good thing.

My example of good bias was ignoring irrelevant variables in #science to avoid spurious or misleading correlations.

Tom’s examples are probable better: fire alarms and #signalDetectionTheory. Worth a read!

#cogSci #philSci #decisionScience #psychology

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rhetoric Feb 3

Common mistakes:
Letting bosses decide everything (fix: use anonymous input).
No tiebreaker (fix: choose who gets the final say upfront).

Try this: Pretend your decision failed - why? Work backwards to spot weak points.

Example: A tech team used scored rankings to choose growth projects, ignoring the highest-paid person’s opinions.

#DecisionScience #ConsultingTips #CriticalThinking #Logic #Persuasion #Influence #Communication #Leadership #Management #CognitiveBias #Psychology (2/2)

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rhetoric Jan 30

#DecisionScience #Leadership #MythBuster #FactCheck #Debunked #ScienceBehindIt #ResearchReveals #EvidenceBased #CriticalThinking #LogicWins #TruthHurts #RealityCheck (3/3)

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rhetoric Jan 28

Founders and leaders: If you want to influence decisions, focus on how minds work, not just spreadsheets.
#MythBusting #FactCheck #Debunked #Neuroscience #Research #Evidence #DecisionScience #CriticalThinking #Facts #Reality (3/3)

Steven Ponce Jan 28

Built an oncology launch curve forecaster. The insight? Stop asking "what will happen" and start asking "what does this assumption cost if we're wrong?"
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🔗 https://tinyurl.com/5cx9s4ye
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#rstats | #shiny | #pharma | #commercialanalytics | #decisionscience

Nick Byrd, Ph.D.Jan 23

The latest #PhilosophyOfScience of #DecisionMaking seminar is starting!

This week: Malvina Ongaro, who will present her paper, “Decision-making for the management of natural risks”

More about the series: https://sites.google.com/view/philscidec/home

#PhilSciDec #Philosophy #Psychology #DecisionScience #CogSci

PhilSciDec

ABOUT Philosophy of Science of Decision Making is an online research seminar run by Dr James Grayot of the Mind, Language, and Action Group (MLAG) of the University of Porto, Institute of Philosophy. The aim of the seminar is to bring together philosophers of science and decision researchers