Nick Byrd, Ph.D.
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Nick Byrd, Ph.D.
@byrdnick.com
3.2K followers 520 following 14K posts
I study how to improve decisions and well-being at @GeisingerCollege.bsky.social.

🎓 gScholar: shorturl.at/uBDPW

▶️ youtube.com/@ByrdNick

👨‍💻 psychologytoday.com/us/blog/upon-reflection

📓 byrdnick.com/blog

🎙️ byrdnick.com/pod
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🏔️ 🦃 I’m headed to the Rockies for the holiday. If I can post about the other talks and posters I attended, they’ll be below.

Thanks to @sjdm-tweets.bsky.social for another great event!

As always, it’s great to meet new smart people, meet people I’ve only ever met online, reconnect with others. ✌️
Denver’s Union Station Denver’s theatre district Denver Convention Center bear
November 24, 2025 at 8:01 PM
byrdnick.com
How can we identify or block #AI #survey respondents?

@andreaaaaa.bsky.social, Kianté Fernandez, @jbogard.bsky.social, and Craig Fox demoed examples, facilitated discussion, and tested our live suggestions.

See their Modern Threats to Online Surveys: kiante-fernandez.github.io/sjdm-data-va...
Some general principles about how AI agents perform on online tasks. The activities tab from the URL in the post. A discussion activity slide.
November 24, 2025 at 6:45 PM
byrdnick.com
Can you test a ton of stimuli without #Python, #Otree, etc.?

Angshuman Pal, Christina Rader, and Asa B. Palley can!

All you need is:
- a spreadsheet (.csv)
- Qualtrics' Directories/Contact list function

Try it yourself with their materials of two examples at osf.io/vy27c/overvi...

#surveyMethods
Imagine you want understand how people respond to Al advice • Participants guess the age of a person in a photo. • They see advice from Al with a number and explanation. • They get to revise their answer. • The photo comes from a pool of 200 photos. • They repeat it six times with six different photos. Questions: In Qualtrics, • How long would it take to set up? • What could go wrong? One-slide Summary • Researchers increasingly need to present stimuli that are highly customized. • Instead of manually adding stimuli into Qualtrics one-by-one, you can upload them, using a "Stimulus Table" to a Qualtrics List. • The Stimulus Table contains details on all combinations of stimuli you want to use. • We detail a six-step process. • We provide examples and a tutorial.
November 24, 2025 at 6:18 PM
byrdnick.com
From this morning's #SJDM25 business meeting:

1. The SJDM journal will now cooperate with @peercommunityin.bsky.social in ways that may enhance peer review.

2. @olegurminsky.bsky.social asked about if there are plans to adopt conventional term limits as we call for new/more JDM journal editors.
The 2025 SJDM conference Business Meeting.
November 24, 2025 at 6:12 PM
byrdnick.com
Does #career advice depend on advisees’ income?

Ibitayo Fadayomi et al. found low-income advisees were guided more toward short-term 💰 gain and less toward long-term career goals.

A low-income advisee reported regreting that approach.

Follow for the pub alert: www.researchgate.net/profile/Ibit...
Two plots showing the following (N = 548 working professionals): The recommend-ability of long-term career success was lower for a low-income advisee than other advisees (p < 0.01). One working professional's explanation of this preference: "Money comes and goes, YOLO, go for the best [long-term career success] my guy, opening up career opportunities is of the utmost importance." Recommend-ability of short-term monetary gains were higher for a low-income advisee (p < 0.01). One working professional's explanation of this preference: "I would lean towards [shorter-term monetary gain], since having financial security has a bigger impact on mental health and safety." The prior pattern replicated in follow-up experiment asking people what internship they should take, before advice was received. After advisees received advice, preferences for shorter-term monetary gains became more salient, moreso for low-income advisees. Why does this matter? One participant explains why: "Being a once low-income student I understand the struggle when deciding between paid and unpaid. The way I would look at it is looking at your end goal. I would assume its to be at the top of your field and work for the best company you can. Having the doors opened for you by Company A would be the best option for you. I missed out on that and struggled to find a job I would enjoy after graduating because I went with the one that was paid at a lesser-known company."
November 23, 2025 at 11:17 PM
byrdnick.com
Should sexual harassment victims be identified? 

What about perpetrators?

Karin Ella Bitman et al. found — contrary to the #IdentifiableVictimEffect — UNidentified victims got MORE help.

But identified PERPS yielded MORE help for the victim.

Karin's on LinkedIn: www.linkedin.com/in/karinella...
Experiment 1 Summary Reversal of the Identified Victim Effect: Unidentified victim gets more help than the Identified victim And the Identified perpetrator elicits more help (for the victim) than the Identified victim. Experiment 2: Effects on Willingness to Donate Experiment 2: Effects on Donation Amount Experiment 2 replicated experiment 1.
November 23, 2025 at 10:30 PM
byrdnick.com
Sidenote: Whoever conceived the build-your-own #TrailMix stand needs a raise.

#SJDM25 #food #conferencing
A "Trail's End" menu of trail mix items: - Coconut granola - Dried cranberries - Golden raisins - Candied walnuts - Roasted almonds - Pecans - Banana chips - M&M's From the Sheraton Downtown Denver at the 2025 SJDM conference (Nov 21-24)
November 23, 2025 at 9:57 PM
byrdnick.com
Skipping medical appointments wastes loads of resources.


So many clinics ask us to (re)confirm our appt.

Telling patients WHY they ask reduced no shows.

That effect correlated with patients’ reported #lossAversion.

Follow Dr. Schwartz to for the pub. alert: www.researchgate.net/profile/Dani...
Background • Collaboration with one of the largest private medical institutions in Chile • Situation before the experiment: Intense use of reminders and opportunities to confirm/cancel (WhatsApp and IVR) - Kept during the experiment This slide also shows the various outcomes of a patient's appointment: - Attend (60%) - No show (40%) Of the 40% of no-shows: - 19% Canceled at least one day prior - 15% had confirmed their appointment (!) - 6% the doctor also didn't show up
November 23, 2025 at 8:57 PM
byrdnick.com
Don also shared his template for declining review requests from journals that try to profit from our taxpayer-funded and volunteer #science:

learnmoore.org/hotfresh.html

That URL also links to a form to share your #HotFresh preprints, data, reviews, etc. to the @sjdm-tweets.bsky.social newsletter.
The template's text: I object to providing my free labor to publishers that rely on the volunteer efforts of authors, reviewers, and editors, claim copyright over our work, restrict the dissemination of scientific knowledge, and then charge our own libraries for access to it. Although I appreciate the valuable role that journal publishers once played, the Internet age has largely eliminated the value of distributing paper journals. I wish I could publish all my work in open-access venues. Yet, perhaps like you, I find myself constrained by the current system while at the same time envisioning a better future. One way to help facilitate change is if individual editors or whole editorial teams move to open-access versions of old-fashioned paper journals. In case you or your editorial team has ever considered such a move, allow me to offer my enthusiastic support. With apologies for being difficult, Don Moore The Drake no/yes meme, except Drake is replaced with an anthropomorphized mouse (a la Stuart little). This AI-generated mouse appeared throughout the slides as a personification of various points about the challenges scientists face, including the increasing politicization of science funding. The mouse first appeared early in the address when Don reminded us of the "transgender mice" remark from the 47th POTUS. The QR code for Don Moore's review declination template on the final slide. The Q&A included pushback: 
Danny Oppenheimer: Would more open publishing allow more misinformation by allowing more junk science to be posted along good science? - Reply: the status quo is preprints and science reporting is not good. Hard to know if openness actually makes things much worse.
 (I didn't see who asked): How else will early career people get hired and promoted if not by prestigious publications? Wasn't Don's early work on overconfidence made famous by a paywalled, profit-seeking Psychology journal article?

 - Reply: Don still feels uncomfortable about that paper’s home. And there are better forms of prestige, such as the prestige one gets for prioritizing rigorous open science, changing one’s mind in light of better evidence, etc.
November 23, 2025 at 7:41 PM
byrdnick.com
...many leaders in the field are still seeking prestige from a few for-profit, rent-seeking journals.



He then pointed us to better publication practices:
- @peercommunityin.bsky.social

- @unjournal.bsky.social

- @metaror.bsky.social

Post credits: @alexh.bsky.social, @jwastrachan.bsky.social
James Strachan @jamesstrachan (Twitter) • Dec 21, 2019 US publishers, inc. APS, APA, Elsevier, Wiley: "This would effectively nationalize the valuable American intellectual property that we produce and force us to give it away to the rest of the world for free." Non-US academics: WTF Quote Tweet: Association of American Publishers @American... • Dec 18, 2019 Proud to join 125+ other organizations to oppose a costly proposed Administration policy that would undermine scientific discovery, American jobs, & our global competitiveness. Read the coalition letter: ..e-production-content.s3.amazonaws.com/uploads/1508/c…. @americanpublish @AmericanCancer @globallPcenter... operating profit | company | industry 7% | Woolworths | supermarkets, pokies 12% | BMW | automobiles 22% | Coca-Cola | adding sugar to water 23% | Rio Tinto | mining 36% | Apple | premium computing 34% | Springer | scholarly publishing 36% | Elsevier | scholarly publishing 40% | Wiley | scholarly publishing http://alexholcombe.wordpress.com/2013/01/09/scholarly-publishers-and-their-high-profits/ Don Moore presenting a slide of better cognitive science publication pathways and practices at the 2025 SJDM conference in Denver Colorado.
November 23, 2025 at 7:41 PM
byrdnick.com
Are #AI tailored messages worth it?

My @geisingercollege.bsky.social colleague @sbrietz.bsky.social shared team #science finding #ML tuned messages beat control messages, but performed similarly to generic “reserved for you” messages.

Follow to get the pub alert: scholar.google.com/citations?us...
Estimating Heterogenous Treatment Effects in the 2020 Megastudy Sasha is presenting a slide illustrating the machine learning pipeline used to tailor vaccination messages to individual patients. Testing Al-Tailored Messaging in a Prospective RCT We randomized Geisinger patients (N = 77,482) with an upcoming flu shot-eligible appointment in the 2024 flu season to one of four arms to be sent: • Algorithmically-selected message determined by trained ML model • Reserved for you message • Active Control message (reminder) • Passive Control (no message) Primary outcome: Whether patients received a flu vaccine between the date of randomization and their scheduled appointment Analysis was intent-to-treat and preregistered (NCT06566534) Messages personalized with the #machineLearning pipeline were no better than the more generic “reserved for you” message, but the tailored messages did beat passive and active control conditions. And these results were robust for patients who: • Enrolled in the 2020 megastudy Scheduled for primary care visits • Sent additional high-risk flu messages • Received their top-predicted message Flu Shot Uptake by Assigned Message Across Arms This plot shows percent who received a flu shot (y-axis) by algorithmically assigned message/nudge with previous years' flu shot percentages overlaid (2019 to 2023).
November 23, 2025 at 6:07 PM
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Before #MSFT offered "Facilitator" in Teams meetings, @dggoldst.bsky.social et al. developed something similar.

People preferred their #statusQuo, but #AI facilitation
- made people more open to both human and #LLM facilitation.
- didn't seem to impact decisions

More via doi.org/10.48550/arX...
Experimental conditions: - No facilitation - Human facilitation - LLM facilitation Human and LLM facilitators receive the same instructions 1. Guiding message "People may have different information about what is being discussed in this meeting, so encourage everyone to share all of the relevant information they have.' 2. Information about the facilitator role "As a facilitator for this meeting, your specific role is help the group make a decision by, first, making sure that everyone is heard from and shares what they know and, second, acting as a scoreboard and keeping track of pros and cons.* Forecasted and actual decisions were similar across conditions. See Figure 7 from the preprint (URL/DOI in the post). Survey question: "You completed this task with a [human, Al, none] facilitator. If you were to repeat this task, which would you prefer?" "Users seem to prefer what they know, whatever it may be" "Experiencing LLM facilitation seems to yield interest in both facilitators, in a way that human facilitation doesn't."
November 23, 2025 at 12:58 AM
byrdnick.com
Agreement can feel nice, but it may discourage reflection and understanding.

Agreeing with someone about a policy (vs disagreeing) predicted lower
- odds of asking, "Why?"

- accuracy in predicting their preferences

Follow Zhiyang (Bella) et al for the pub alert: scholar.google.com/citations?us...
People Who Agreed on a Position Varied in Their Reasons Position: "We should increase restrictions on foreign ownership of U.S. land and businesses." Reasons: - Limits foreign influence 22% - Protects national sovereignty 30% - Preserves economic control 25% - Prevents land exploitation and misuse 20% - Others 3% Study 1: Do We Ask Why When We Agree? Plot of the percentage of people that asked why (y-axis) by agreement (x-axis): When partner agreed: 25% When partners disagreed: 70% Study 3: The Learning Loss Incurred by Not Asking Why When We Agree When People Agreed, They Were Worse at Predicting Their Counterpart's Future Preference: 48% versus 37% (p = 0.023).
November 23, 2025 at 12:12 AM
byrdnick.com
How do people think about using #AI for #writing?

Andras Molnar and Jiaqi Zhu found people didn’t suspect #LLM use without cues or reminders, which DID hinder impressions (2 experiments, N = 1301, 8 contexts).

Follow for the pub alert: www.researchgate.net/profile/Jiaq...

#jobMarket #socialPsych
How Al may erode social signals in writing Three qualities make an observable signal to be perceived as credible: 1. Costly Not anymore. Generating content has become trivially easy, and requires less energy: 130-1500 times less CO2/page of text than human writers (Tomlinson et al., 2024) 2. Hard to fake Not anymore. Generative Al is remarkably effective at creating personalized, context-dependent content 3. Verifiable Not anymore. We generally lack the tools to reliably detect Al-generated content (Köbis & Mossink, 2021; Kreps et al., 2022; Sadasivan et al., 2023; Tang et al., 2023; Jakesch et al., 2023; Gao et al., 2024; Porter & Machery, 2024) Uncertainty & Limited Attention What if audiences are aware of potential Al use but remain uncertain about actual Al use? (a classic signaling problem) What if they don't even think about it? (i.e., default judgments) Attentional constraints, bounded rationality (Simon, 1955; Slovic, 1972) - "What you see is all there is": Decision problems are constrained by their presentation and what is most salient in our minds (Kahneman, 2011; Enke, 2020) Does participants' own use of genAl moderate these effects? "Al disclosure penalty" (human vs. Al): YES Default judgments (human vs. no info): NO The slide shows a plot showing "overall social impression" (y-axis) by message source (x-axis: no info, human-written, uncertain, and AI-generated). The AI-generated source cue had a huge negative impact (d > 1, p < 0.001).
November 22, 2025 at 11:24 PM
byrdnick.com
Rating scales are vulnerable to biases that reduce predictive power.

Can #languageModels do better by analyzing open-ended responses?

@adaaka.bsky.social found such #LLM measures were more predictive and less vulnerable to #bias in 7 preregistered studies (N = 2326).

#psychometrics #tech #PhilSci
Can open-ended responses, analyzed with LLMs, help overcome these challenges? → Open-ended format does not bring certain information to mind. participants generate the content they consider most important → This may help minimize order effects and increase predictive power → A single open-ended question can contain multiple psychological dimensions → LLMs can extract latent structure at scale (e.g., protocol analyses as discussed by Ericsson & Simon, 1993) From a within-subject design: - corr(global life satisfaction rating, GPT inferred life satisfaction) = 0.76 - corr(global life satisfaction rating, relationship satisfaction rating) = 0.46 - corr(global life satisfaction rating, GPT-inferred relationship satisfaction) = 0.58 From a between-subject design: - corr(global life satisfaction rating, GPT inferred life satisfaction) = 0.63 - corr(global life satisfaction rating, GPT-inferred relationship satisfaction) = 0.52 Other domains show similar effects: → Rating life satisfaction and 8 dimensions of life satisfaction (within subjects design with Stanford students) - Largest effect for: health (r = 0.60 vs r = 0.77) and academic life (r = 0.57 vs. r = 0.74) → Future outlook of Al (between subjects design on Prolific) - Largest effect for: inequality concerns (r= 0.43 vs. r = 0.76) → Smartphone satisfaction (between subjects design on Prolific) - Largest effect for price (r= 0.30 vs. r = 0.64) - No difference for battery concerns
November 22, 2025 at 10:24 PM
byrdnick.com
Another way to see inside the black box of #decisionMaking involves recording people thinking aloud during decisions.

@aaronlob.bsky.social and @renatofrey.mstdn.science.ap.brid.gy had #AI predict people’s perceptions from their verbalizations (N = 178).

The correlations ranged from ≅0.3 to ≅0.6.
Research question 2: Can large language models predict the perceptions people reported about the real-life decisions they verbalized? An illustration of how a language model can "directly" infer a decision-makers self-reported perceptions based solely on the decision-makers verbalization of the decision (and not the self-reported perception). The three methods used to have language models infer people's self-reported perceptions from the decision-makers think-aloud recording of the decision: direct, feature extraction, and embeddings. Visualization of the correlations between model-predicted and self-reported perceptions (left to right) by method (top to bottom). Correlations range from 0.26 to 0.59.
November 22, 2025 at 9:52 PM
byrdnick.com
Many medical systems remind patients about their #health gaps with text messages.

Would adding info about the risks or available benefits increase response rates?

Not in the experiments from Jose Arellano Martorellet et al.!

Follow to get the publication alert: scholar.google.com/citations?us...
RCTs 2-3: Alerting patients about health gaps Context: Prompting primary care patients with overdue preventive health gaps. - Cancer screenings - Diabetes exams - Annual physical exam - Blood pressure control Impact of reminders on patients who have not opened the letter after the initial notification. Pre-registrations on ClinicalTrials.gov: NCT05791942, NCT05979987 RCT 3: Experimental design Results (N = 6,893 patients) Conclusions • Field evidence of information avoidance in response to health messaging. • Making health messages more attention-grabbing by increasing their importance may come with a psychological cost that unintentionally backfire Implications • Practical implications for healthcare systems using health-related outreaches • Implications for the ongoing debate on nudges in policy
November 22, 2025 at 9:04 PM
byrdnick.com
Many cognitive scientists change decisions; few look in the black box to see HOW.

Steph Smith demoed such #processTracing
...in #Qualtrics!
…without any coding!

Find everything you need at github.com/QMT-code/QMT

Follow her work at www.researchgate.net/profile/Step...

#eyeTracking #mouseTracking
Qualtrics Mouse Tracing (QMT) may be the only scalable, code-free way to track mouse activity during experiments, according to Steph Smith (U Chicago). "If you want complete and total flexibility for your study design, we have a step-by-step version with extensive documentation!" – Steph Smith (U Chicago) Does the search pattern predict choice? (Yes) • Outcome: Proportion of choices for higher expected-value option • Predictor: Payne Index {option-wise transitions - attribute-wise transitions} Steph Smith's collaborator is David Dolifka. Find their code, Qualtrics files, etc. on GitHub with the QR code or the URL in the post.
November 22, 2025 at 8:44 PM
byrdnick.com
🧠🏔️ Below I'll share mine and others' presentations from the Society for Judgment and Decision Making conference in #Denver.

Did you attend a session I missed?
Did I fail to tag a presenter?
Feel free to add to the thread!

Long live #openAccess conferencing.

#SJDM #SJDM25 @sjdm-tweets.bsky.social
Byrd, N. (2025, July). Map My Words—Using Waitlist Controlled Trials To Test Whether Argument Mapping Improves Individuals’ Persuasive Writing or Critical Thinking. Experimental Argument Analysis, University of East Anglia. https://www.researchgate.net/publication/390977878 Abstract. Argument mapping is the practice of diagraming the logical relationships between each proposition in an argument, including objections and counter-objections. Some studies find that courses that teach students how to map arguments exhibit better critical thinking and persuasive writing than students in other courses. These promising results are sometimes from surveyors of argument mapping goods and services, and they garner plenty of attention in fields that champion careful thinking and communication. However, the total evidence is mixed, null results are often never published, and many promising studies have not controlled for known confounds. Two waitlist control trials (N = 83) attempted to address these issues; they detected no benefits of argument map training within or between groups (p > 0.13). Rather, variance in outcomes was explained by enrollment in the instructor’s other courses, prior academic achievement, and — most robustly — engagement in flipped classroom activities. These findings raise questions about whether benefits previously attributed to argument mapping in less controlled studies were caused by other factors that were already known to produce such benefits. Implications for pedagogy, course assessment, and the science of learning are discussed.
November 22, 2025 at 6:35 PM
byrdnick.com
Years after testing my own positive expectations about argument mapping, I sometimes forget just how strong the allure of argument mapping can be.

Should I find a way to work these forecasts into the paper?

www.researchgate.net/publication/...

#logic #argumentMapping #edu #criticalThinking #tech
Twitter poll: "Which logic module do you think will *better* improve people's critical thinking and argumentation?" Truth tables 15.9% Argument maps 51.2% The won't be different 11.6% I wouldn't know 21.3% 164 votes LinkedIn poll: "Which logic module do you think will *better* improve people's critical thinking and argumentation?" Truth tables 22% Argument maps 48% The won't be different 7% I wouldn't know 22% 27 votes Mastodon poll: "Which logic module do you think will *better* improve people's critical thinking and argumentation?" Truth tables 22% Argument maps 31% The won't be different 0% I wouldn't know 47% 32 votes
November 20, 2025 at 2:48 AM
byrdnick.com
Will #AI models think better if we help them reflect on frameworks from #ethics, #law, or #psychology?

This paper found the such "reasoning theories" usually helped, but could also hinder debiasing or accuracy depending on the context and on the model.

www.iiia.csic.es/med...
The reasoning theories and Figure 2: "System Prompt Architecture Overview. The modular design spans three columns: (1) Common structural elements shared across all theories (blue/green), (2) Theory-specific content and evaluation protocols (orange), and (3) Evaluator personalities and structural enforcement mechanisms (orange/green). Each theory provides specialized evaluator characteristics while maintaining a consistent pipeline." • Bias Mitigation: ...the Bias Benchmark for Question Answering (BBQ) assesses stereotypical associations across demographic categories including age, gender, religion, and nationality. • Truthfulness Assessment: ...TruthfulQA... evaluates models’ tendency to generate false statements that mimic human misconceptions. • Adversarial Robustness: ...AIR-Bench [is] a standardized benchmark for automated red-teaming. "Our empirical results show complex interactions between reasoning theories, evaluation tasks, and model architectures." "Table 1 summarizes model performance on BBQ and TruthfulQA, reporting tentative accuracy..., final accuracy..., and revision metrics." "5.2. Analysis of Adversarial Robustness Table 2 presents comprehensive safety performance metrics on the AirBench benchmark, which evaluates model resistance to harmful prompts through Attack Success Rate (ASR), Soft Reject Rate (SRR), and Clear Reject Rate (CRR)." "Key Findings Our analysis suggests four key observations: (1) Dual-role architectures tend to improve performance over single-stage reasoning in several tasks, with benefits appearing even in simple implementations. (2) Basic architectural changes often provide similar benefits to more complex theoretical approaches. (3) Theory effectiveness appears highly dependent on task type, where skeptical mechanisms may help with fact-checking but potentially harm contextual reasoning. (4) The observed patterns suggest that theory selection could serve as a useful tool for balancing precision and recall in specific applications."
November 10, 2025 at 12:47 PM
byrdnick.com
How many generations into the future should we consider when making collective policy decisions?

Seven? More? Less?

Averaging across thousands of people in the U.S., moral *concern* extended 10 generations, but *obligation* extended about twice as much!

doi.org/10.1038/s415...
"To start, we find that concern extends beyond the present for most average Americans, regardless of how this was measured. Subjects report that individuals and governments alike ought to consider approximately 10 generations ahead when making collective decisions. They also report feeling at least some moral concern for approximately 30 future generations, the same point at which average perceptions of obligation to future generations drop to half their magnitude relative to the present. Intriguingly, obligation persists for twice as many future generations as moral concern, suggesting perceptions of responsibility may endure even in the absence of personal feelings of concern for beneficiaries." Law, K. F., Syropoulos, S., Young, L., & O’Connor, B. B. (2025). Mapping prescriptive beliefs on seventh generation stewardship and increasing the temporal scope of intergenerational concern. Scientific Reports, 15(1), 37618. https://doi.org/10.1038/s41598-025-21466-9
November 4, 2025 at 11:57 AM
byrdnick.com
Notably, some of the investigators had "a prior belief in the positive effects of #prayer" (Benson and Byrd) and nonetheless found plenty of null results, or results that hovered on either side of the null (p. 16, 👆).

🔓 pmc.ncbi.nlm.nih.gov/articles/PMC...

#religion #health #cogSci #stats #philSci
“The Benson 2006 and Byrd 1988 studies were undertaken by researchers with a prior belief in the positive effects of prayer. The beliefs of the researchers in the other trials are not clear.” Roberts, L., Ahmed, I., Hall, S., & Davison, A. (2009). Intercessory prayer for the alleviation of ill health. The Cochrane Database of Systematic Reviews, 2009(2), CD000368. https://doi.org/10.1002/14651858.CD000368.pub3
November 3, 2025 at 2:37 PM
byrdnick.com
Prayer's effects on health have been studied many times.

Small studies initially reported some benefit, but more rigorous studies found null or even harmful effects.

In sum, prayer seemed ineffective, recommending focus on more promising interventions: doi.org/10.1002/1465...
"Ten studies are included in this updated review (7646 patients). ...overall there was no clear effect of intercessory prayer on death... (6 RCTs, n=6784, random-effects RR 0.77 CI 0.51 to 1.16, I(2) 83%). For general clinical state there was also no significant difference between groups (5 RCTs, n=2705, RR intermediate or bad outcome 0.98 CI 0.86 to 1.11). Four studies found no effect for re-admission to Coronary Care Unit (4 RCTs, n=2644, RR 1.00 CI 0.77 to 1.30). Two other trials found intercessory prayer had no effect on re-hospitalisation (2 RCTs, n=1155, RR 0.93 CI 0.71 to 1.22). ...although some of the results of individual studies suggest a positive effect of intercessory prayer, the majority do not and the evidence does not support a recommendation either in favour or against the use of intercessory prayer. We are not convinced that further trials of this intervention should be undertaken and prefer to see resources ...used to investigate other questions in health care."
November 3, 2025 at 12:22 PM
byrdnick.com
What did I learn from this year's annual blood test results?

I shouldn’t run 40 miles the day before I get the blood drawn…

…unless I tell my doctor about the run *before* they see the results. 😆

pubmed.ncbi.nlm.nih....

#exercise #running #medicine #health #hematology #edu
"Physicians are conditioned to interpret blood test results outside "normal" limits as being signs of disease. In endurance-trained athletes, such "abnormal" tests results may instead be indications of physical activity. Data from six runners are presented illustrating that anemia, liver disease, myocardial ischemia, or renal disease might erroneously be diagnosed unless the physician is aware of the exercise habits of the patient." Bunch T. W. (1980). Blood test abnormalities in runners. Mayo Clinic proceedings, 55(2), 113–117.
October 31, 2025 at 11:32 AM