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Why Your Digital Detox Resolution Fails by January 15

Every January, millions of people make the same promise: “This year I’m going to spend less time on my phone.” By mid-month, most are back to doomscrolling in bed, feeling like they’ve failed yet another resolution. According to Offline.now founder and author Eli Singer, that story is not about laziness, it’s about confidence. Offline.now’s proprietary research shows 8 in 10 people want to change their relationship with technology, but more than half feel so overwhelmed by their habits they don’t know where to start. “If you don’t learn how to manage the screens in your life, they will manage you,” says Singer. “When people tell us they feel overwhelmed, it’s not laziness. It’s a crisis of confidence. And confidence is something that can be built.” At the heart of the platform is the Offline.now Matrix, a behavioral framework that maps people into four quadrants: Overwhelmed, Ready, Stuck, or Unconcerned - based on their motivation and confidence levels. Someone who is “Overwhelmed” needs reassurance and tiny first steps; someone who is “Ready” can handle bigger commitments. Treating everyone as if they’re in the same place (“just delete Instagram”) virtually guarantees most resolutions will collapse. Psychotherapist Harshi Sritharan, MSW, RSW, who specializes in ADHD and modern anxiety, sees how this plays out in the brain. For many of her clients, especially those with ADHD, digital devices provide a fast dopamine hit that everyday life simply can’t match. “With ADHD, you’re working with a dopamine deficiency,” she explains. “Phones and apps are designed to give you highly stimulating, personalized content. You get this huge dopamine surge, and when you put the device down, everything else feels flat, boring and harder to start.” She notes that common habits like checking your phone the second you wake up, quietly undermine even the best January intentions: “If you’re on your phone first thing in the morning, you hijack your attention and dopamine for the rest of the day. Your brain has already tasted the highest stimulation it’s going to get, and it will keep seeking that level. That’s not a willpower issue, it’s neuroscience.” The good news: the science suggests you don’t need a perfect detox to see benefits. A JAMA Network Open study on young adults found that reducing social media use for just one week - without going completely offline; led to about a 24.8% drop in depression, a 16.1% drop in anxiety, and a 14.5% drop in insomnia symptoms. “Lasting change doesn’t require deleting Instagram or TikTok tomorrow,” says Singer. “You need to win one personal victory today, and then another tomorrow. That’s how confidence rebuilds.” Featured Experts Eli Singer – Founder of Offline.now and author of Offline.now: A Practical Guide to Healthy Digital Balance. Speaks to the behavioral data behind failed resolutions, the confidence gap, and the Offline.now Matrix framework. Harshi Sritharan, MSW, RSW – Psychotherapist specializing in ADHD, anxiety and digital dependency. Explains the dopamine science behind compulsive scrolling and offers brain-friendly strategies that work better than “willpower.” Expert interviews can be arranged through the Offline.now media team.

With the MOMitor™ app, Florida mothers have better maternal care right at their fingertips

A program spearheaded by University of Florida physicians recently expanded to improve care for new mothers throughout the state, using tools they have right at home. Five years ago, a team of obstetricians and researchers at the UF College of Medicine launched MOMitor™, a smartphone app that allows new mothers to answer health screening questions and check vitals like blood pressure in the comfort of their own homes, using tools given to them by their health care providers. Depending on the data, the clinical team can then follow up with patients as needed for further medical intervention. Now, the app is expanding beyond North Central Florida — where nearly 4,400 mothers have participated in the program — to other areas in the state. Clinicians are also teaming up with data scientists at the College of Medicine who are using artificial intelligence to study data and identify trends that can lead to more personalized care. Program expansion Thanks to funding from the Florida Department of Health to support the state’s Telehealth Maternity Care Program, MOMitor™ has recently expanded for use in Citrus, Hernando, Sumter, Flagler, Volusia, Martin, St. Lucie and Okeechobee counties, said Kay Roussos-Ross, M.D. ’02, MPAS ’98, a UF professor of obstetrics/gynecology and psychiatry who is leading the program. “The Florida Legislature was really motivated and interested in improving maternal morbidity and mortality, and through this program we’re touching additional parts of the state and helping patients beyond North Central Florida,” she said. Maternal mortality is a serious concern in the United States, with more than 18 deaths recorded per 100,000 births in 2023, according to the latest data available from the U.S. Centers for Disease Control and Prevention. This is a much higher rate than most other developed countries, Roussos-Ross said. Common factors that may lead to maternal mortality, which is measured from pregnancy through the first year after giving birth, include infection, mental health conditions, cardiovascular conditions and endocrine disorders. Many of these complications can go unnoticed or unmonitored, particularly if at-risk mothers are not reporting complications to clinicians. A 2025 study published in the Journal of the American Medical Association shows that up to 40% of women do not attend postpartum visits. “By leveraging AI, we have the opportunity to target moms and moms-to-be who might be at greater risk of complications ... and encourage them to participate in the program to mitigate these.” — Tanja Magoc, Ph.D. “Whereas we’re used to seeing patients pretty routinely during pregnancy, after delivery visits quickly drop off and some women don’t make it back for postpartum care, so we may not have an opportunity to continue supporting them,” Roussos-Ross said. “This can often be because of barriers such as housing, transportation or food insecurity. We offer referrals to help with some of these services.” With MOMitor™, patients can let their clinician know how they are recovering without visiting the clinic, improving access to care in situations where that is not always an easy option for new mothers. “It’s a way to be proactive,” Roussos-Ross said. “Instead of waiting for a patient to come to us when they haven’t been doing well for a while, we connect with them through the app and follow up when they initially begin not doing well, so we can address concerns more quickly.” Studying data to personalize care Roussos-Ross’ team is collaborating with data scientists from the College of Medicine’s Quality and Patient Safety initiative, or QPSi, to determine how AI can assist in finding ways to further improve processes. “By leveraging AI, we have the opportunity to target moms and moms-to-be who might be at greater risk of complications, such as developing postpartum depression or hypertension, and encourage them to participate in the program to mitigate these complications,” said Tanja Magoc, Ph.D., the associate director of QPSi’s Artificial Intelligence/Quality Improvement Program. David Hall, Ph.D., a QPSi data scientist, said his team is working alongside the clinical team to analyze data that can be used to create recommendations for patients. “Everything we do comes from information supported in the patients’ charts,” Hall said. “We also make sure the data upholds compliance standards and protects patients’ privacy.” “We’re interested in finding out what areas might be hot spots and determining what makes them this way, so we can ... better identify areas where there may be high-risk patients and provide interventions to those who need it most.” — David Hall, Ph.D. The teams aim to intervene before patients encounter postpartum complications, addressing potential issues before they become significant problems. After taking into account a patient’s personal and family medical history, the team looks at information such as geolocation, drilling down to areas much smaller than the ZIP code level in order to find points of potential concern. “We’re interested in finding out what areas might be hot spots and determining what makes them this way, so we can study these patterns throughout the state and better identify areas where there may be high-risk patients and provide interventions to those who need it most,” Hall said. Roussos-Ross said she is proud of the work her team has done to improve patient outcomes through the program so far and is excited to empower more patients. “Every year, the participants give us recommendations on how to improve the app, which we love. But they also say, ‘This is so great. It helped me think about myself and not just my baby. It helped me learn about taking care of my own health. It made me remember I’m important too, and it’s not just about the baby,’” Roussos-Ross said. “And that is so gratifying, because women are willing to do anything to ensure the health of their baby, sometimes at the expense of their own care. This is a way for us to let them know they are still important, and we care about their health as well.”

New book from Aston University academic shows that Christmas tasks mostly fall on women

New book by Dr Emily Christopher shows differences in how household tasks are divided by men and women Book highlights that women tend to buy the Christmas presents and send cards Men often see women as being more thoughtful or having better knowledge of what people would like. A new book from Aston University’s Dr Emily Christopher reveals that when it comes to sending Christmas cards and buying Christmas presents, women are still mostly doing the work as they are perceived to have better knowledge of what people would like. Dr Emily Christopher, a lecturer in sociology and policy at Aston School of Law and Social Sciences, has recently published her book Couples at Work: Negotiating Paid Employment, Housework and Childcare, which look at how household tasks are divided by men and women and the reasons behind these divisions. The data for the book has been collated over an eight-year period with couples being interviewed twice to provide a robust set of results. It looks at how different sex parent couples combined paid work, housework and childcare. The research revealed how gender norms continue to shape how certain daily household jobs are divided. Women were more likely than men to clean the house, especially bathrooms, wash clothes and put clothes away. Men still tend to do tasks like mowing the lawn and DIY but now are also more likely to do the cooking and the grocery shopping. The research shows that the key to understanding how household tasks are divided lies in the meaning they hold for partners. With the festive season upon us, the book reveals that woman are largely responsible for the Christmas present buying and sending cards with 100% of those taking part in the research saying that women mostly carried out these tasks. This also included buying for the male partner's relatives. In instances where men had a 'helping' role in these tasks, this included being involved in the discussion or consulting on choice of presents, especially for children, with only a small minority buying presents for their own family. The data revealed that where women didn't choose and buy presents for their partners family, they were still involved in reminding their partners that this needed to be done or advising on choice of gifts, showing that women were still taking on the mental load of planning for the festive season. The book reveals that when men were questioned about why they didn't get involved in present buying, they drew on gender norms. Women were often described, by the men, as being more thoughtful or having better knowledge of what people would like. Men often described how family members wouldn’t receive presents at all if it relied on them. Although much of the gift giving and organising represented love and affection for the women, which many found enjoyable, many still saw it as work and expressed that they would like their partners' to be more involved. Dr Christopher said: “This book takes an in-depth look at the way in which everyday roles around the household are divided between men and women. “The research shows that over a period of eight years fathers increased their role in childcare tasks but this did not always extend to housework. “The pandemic was an opportunity to change how couples share housework but women were still more likely to carry out tasks like cleaning, washing clothes and putting clothes away and overwhelmingly remained responsible for the mental orchestration of family work.”

ChatGPT-5.2 Now Achieves “Expert-Level” Performance — Is this the Holiday Gift Research Communications Professionals Needed?

With OpenAI’s latest release, GPT-5.2, AI has crossed an important threshold in performance on professional knowledge-work benchmarks. Peter Evans, Co-Founder & CEO of ExpertFile, outlines how these technologies will fundamentally improve research communications and shares tips and prompts for PR pros. OpenAI has just launched GPT-5.2, describing it as its most capable AI model yet for professional knowledge work — with significantly improved accuracy on tasks like creating spreadsheets, building presentations, interpreting images, and handling complex multistep workflows. And based on our internal testing, we're really impressed. For communications professionals in higher education, non-profits, and R&D-focused industries, this isn’t just another tech upgrade — it’s a meaningful step forward in addressing the “research translation gap” that can slow storytelling and media outreach. According to OpenAI, GPT-5.2 represents measurable gains on benchmarks designed to mirror real work tasks.  In many evaluations, it matches or exceeds the performance of human professionals. Also, before you hit reply with “Actually, the best model is…” — yes, we know. ChatGPT-5.2 isn’t the only game in town, and it’s definitely not the only tool we use. Our ExpertFile platform uses AI throughout, and I personally bounce between Claude 4.5, Gemini, Perplexity, NotebookLM, and more specialized models depending on the job to be done. LLM performance right now is a full-contact horserace — today’s winner can be tomorrow’s “remember when,” so we’re not trying to boil the ocean with endless comparisons. We’re spotlighting GPT-5.2 because it marks a meaningful step forward in the exact areas research comms teams care about: reliability, long-document work, multi-step tasks, and interpreting visuals and data. Most importantly, we want this info in your hands because a surprising number of comms pros we meet still carry real fear about AI — and long term, that’s not a good thing. Used responsibly, these tools can help you translate research faster, find stronger story angles, and ship more high-quality work without burning out. When "Too Much" AI Power Might Be Exactly What You Need AI expert Allie K. Miller's candid but positive review of an early testing version of ChatGPT 5.2 highlights what she sees as drawbacks for casual users: "outputs that are too long, too structured, and too exhaustive."  She goes on to say that in her tests, she observed that ChatGPT-5,2 "stays with a line of thought longer and pushes into edge cases instead of skating on the surface." Fair enough. All good points that Allie Miller makes (see above).  However, for communications professionals, these so-called "downsides" for casual users are precisely the capabilities we need. When you're assessing complex research and developing strategic messaging for a variety of important audiences, you want an AI that fits Miller's observation that GPT-5.2 feels like "AI as a serious analyst" rather than "a friendly companion." That's not a critique of our world—it's a job description for comms pros working in sectors like higher education and healthcare. Deep research tools that refuse to take shortcuts are exactly what research communicators need.  So let's talk more specifically about how comms pros can think about these new capabilities: 1. AI is Your New Speed-Reading Superpower for Research That means you can upload an entire NIH grant, a full clinical trial protocol, or a complex environmental impact study and ask the model to highlight where key insights — like an unexpected finding — are discussed. It can do this in a fraction of the time it would take a human reader. This isn’t about being lazy. It’s about using AI to assemble a lot of tedious information you need to craft compelling stories while teams still parse dense text manually. 2. The Chart Whisperer You’ve Been Waiting For We’ve all been there — squinting at a graph of scientific data that looks like abstract art, waiting for the lead researcher to clarify what those error bars actually mean. Recent improvements in how GPT-5.2 handles scientific figures and charts show stronger performance on multimodal reasoning tasks, indicating better ability to interpret and describe visual information like graphs and diagrams.  With these capabilities, you can unlock the data behind visuals and turn them into narrative elements that resonate with audiences. 3. A Connection Machine That Finds Stories Where Others See Statistics Great science communication isn’t about dumbing things down — it’s about building bridges between technical ideas and the broader public. GPT-5.2 shows notable improvements in abstract reasoning compared with earlier versions, based on internal evaluations on academic reasoning benchmarks.  For example, teams working on novel materials science or emerging health technologies can use this reasoning capability to highlight connections between technical results and real-world impact — something that previously required hours of interpretive work. These gains help the AI spot patterns and relationships that can form the basis of compelling storytelling. 4. Accuracy That Gives You More Peace of Mind...When Coupled With Human Oversight Let’s address the elephant in the room: AI hallucinations. You’ve probably heard the horror stories — press releases that cited a study that didn’t exist, or a “quote” that was never said by an expert. GPT-5.2 has meaningfully reduced error rates compared with its predecessor, by a substantial margin, according to OpenAI  Even with all these improvements, human review with your experts and careful editing remain essential, especially for anything that will be published or shared externally. 5. The Speed Factor: When “Urgent” Actually Means Urgent With the speed of media today, being second often means being irrelevant.  GPT-5.2’s performance on workflow-oriented evaluations suggests it can synthesize information far more quickly than manual review, freeing up a lot more time for strategic work.  While deeper reasoning and longer contexts — the kinds of tasks that matter most in research translation — require more processing time and costs continue to improve. Savvy communications teams will adopt a tiered approach: using faster models of AI for simple tasks such as social posts and routine responses, and using reasoning-optimized settings for deep research. Your Action Plan: The GPT-5.2 Playbook for Comms Pros Here’s a tactical checklist to help your team capitalize on these advances. #1 Select the Right AI Model for the Job: Lowers time and costs • Use fast, general configurations for routine content • Use reasoning-optimized configurations for complex synthesis and deep document understanding • Use higher-accuracy configurations for high-stakes projects #2 Find Hidden Ideas Beyond the Abstract: Deeper Reasoning Models do the Heavy Work • Upload complete PDFs — not just the 2-page summary you were given • Use deeper reasoning configurations to let the model work through the material Try these prompts in ChatGPT5.2 “What exactly did the researchers say about this unexpected discovery that would be of interest to my <target audience>? Provide quotes and page references where possible.” “Identify and explain the research methodology used in this study, with references to specific sections.” “Identify where the authors discuss limitations of the study.” “Explain how this research may lead to further studies or real-world benefits, in terms relatable to a general audience.” #3 Unlock Your Story Leverage improvements in pattern recognition and reasoning. Try these prompts: “Using abstract reasoning, find three unexpected analogies that explain this complex concept to a general audience.” “What questions could the researchers answer in an interview that would help us develop richer story angles?” #4 Change the Way You Write Captions Take advantage of the way ChatGPT-5.2 translates processes and reasons about images, charts, diagrams, and other visuals far more effectively. Try these prompts: Clinical Trial Graphs: “Analyze this uploaded trial results graph upload image. Identify key trends, and comparisons to controls, then draft a 150-word donor summary with plain-language explanations and suggested captions suitable for donor communications.” Medical Diagrams: “Interpret these uploaded images. Extract diagnostic insights, highlight innovations, and generate a patient-friendly explainer: bullet points plus one visual caption.” A Word of Caution: Keep Experts in the Loop to Verify Information Even with improved reliability, outputs should be treated as drafts.  If your team does not yet have formal AI use policies, it's time to get started, because governance will be critical as AI use scales in 2026 and beyond.  A trust-but-verify policy with experts treats AI as a co-pilot — helpful for heavy lifting — while humans remain accountable for approval and publication.  The Importance of Humans (aka The Good News) Remember: the future of research communication isn’t about AI taking over — it’s about AI empowering us to do the strategic, human work that machines cannot. That includes: • Building relationships across your institution • Engaging researchers in storytelling • Discovering narrative opportunities • Turning discoveries into compelling narratives that influence audiences With improvements in speed, reasoning, and reliability, the question isn’t whether AI can help — it’s what research stories you’ll uncover next to shape public understanding and impact. FAQ How is AI changing expectations for accuracy in research and institutional communications? AI is shifting expectations from “fast output” to defensible accuracy. Better reasoning means fewer errors in research summaries, policy briefs, and expert content—especially when you’re working from long PDFs, complex methods, or dense results. The new baseline is: clear claims, traceable sources, and human review before publishing. ⸻ Why does deeper AI reasoning matter for communications teams working with experts and research content? Comms teams translate multi-disciplinary research into messaging that must withstand scrutiny. Deeper reasoning helps AI connect findings to real-world relevance, flag uncertainty, and maintain nuance instead of flattening meaning. The result is work that’s easier to defend with media, leadership, donors, and the public—when paired with expert verification. ⸻ When should communications professionals use advanced AI instead of lightweight AI tools? Use lightweight tools for brainstorming, social drafts, headlines, and quick rewrites. Use advanced, reasoning-optimized AI for high-stakes deliverables: executive briefings, research positioning, policy-sensitive messaging, media statements, and anything where a mistake could create reputational, compliance, or scientific credibility risk. Treat advanced AI as your “analyst,” not your autopilot. ⸻ How can media relations teams use AI to find stronger story angles beyond the abstract? AI can scan full papers, grants, protocols, and appendices to surface where the real story lives: unexpected findings, practical implications, limitations, and unanswered questions that prompt great interviews. Ask it to map angles by audience (public, policy, donors, clinicians) and to point to the exact sections that support each angle. ⸻ How should higher-ed comms teams use AI without breaking embargoes or media timing? AI can speed prep work—backgrounders, Q&A, lay summaries, caption drafts—before embargo lifts. The rule is simple: treat embargoed material like any sensitive document. Use approved tools, restrict sharing, and avoid pasting embargoed text into unapproved systems. Use AI to build assets early, then finalize post-approval at release time. ⸻ What’s the best way to keep faculty “in the loop” while still moving fast with AI? Use AI to produce review-friendly drafts that reduce load on researchers: short summaries, suggested quotes clearly marked as drafts, and a checklist of claims needing verification (numbers, methods, limitations). Then route to the expert with specific questions, not a wall of text. This keeps approvals faster while protecting scientific accuracy and trust. ⸻ How should teams handle charts, figures, and visual data in research communications? AI can turn “chart confusion” into narrative—if you prompt for precision. Ask it to identify trends, group comparisons, and what the figure does not show (limitations, missing context). Then verify with the researcher, especially anything involving significance, controls, effect size, or causality. Use the output to write captions that are accurate and accessible. ⸻ Do we need an AI Use policy in comms and media relations—and what should it include? Yes—because adoption scales faster than risk awareness. A practical policy should define: approved tools, what data is restricted, required human review steps, standards for citing sources/page references, rules for drafting quotes, and escalation paths for sensitive topics (health, legal, crisis). Clear guardrails reduce fear and prevent preventable reputational mistakes. If you’re using AI to move faster on research translation, the next bottleneck is usually the same one for many PR and Comm Pros: making your experts more discoverable in Generative Search, your website, and other media. ExpertFile helps media relations and digital teams organize their expert content by topics, keep detailed profiles current, and respond faster to source requests—so you can boost your AI citations and land more coverage with less work.                                            For more information visit us at www.expertfile.com

Pushing the edge of computing, magneto-ionics imagines efficient processing for AI with reduced resource consumption

Artificial intelligence is a resource-intensive technology. A paper recently published in Nano Letters by collaborators at the Virginia Commonwealth University (VCU) College of Engineering and Georgetown University hopes to improve AI’s ability to parse the vast amounts of information it creates by applying magneto-ionics to the established concept of physical reservoir computing (PRC). “Demonstrating we can make solid-state devices with magneto-ionic materials is an important step into further energy-efficient computing research, and this Nano Letters publication reinforces that,” said Muhammad (Md.) Mahadi Rajib, Ph.D., a postdoc with Jayasimha Atulasimha, Ph.D., Engineering Foundation Professor in the Department of Mechanical & Nuclear Engineering. What makes a decision? Our brains make countless complex decisions everyday. Input comes in, we weigh options and decide what to do. Within that simple path are countless identical loops of input, consideration and output as neurons fire in a chain that takes you from cause to effect. For artificial intelligence, nodes within a neural network receive inputs and provide output, much like the neurons in our brains. These outputs can be sent to other nodes for continued processing, but those outputs need weight to have value. For AI, weight signifies one input or connection is more important than another. Traditional neural networks have multiple layers consisting of countless nodes like this. Each node requires training in order to weigh things properly. Training consumes processing power, and processing power takes time and energy. Making tasks like analysis and prediction more efficient is how to continuously improve AI technology. Less training, more efficiency. Physical reservoir computing reduces the number of nodes an AI needs to train. Only the final output layer needs training in PRC, using a simple method for classification or prediction tasks. A physical “black box” replaces neural network nodes and synapses, like the ones used for AI inference, in PRC and processes inputs by implementing a nonlinear mathematical function with temporal memory. To explain the inner workings of the black box, imagine two stones thrown into still water. One stone is thrown with high force and the other with low force, creating big and small ripples respectively. If the stones are thrown so the second stone lands before the previous ripples have dissipated, the new ripple is affected by the earlier one. This illustrates the concept of temporal memory. In this analogy, if multiple stones are thrown one after another into still water according to some complex trend, observing the ripples over time allows you to understand the trend and train a simple set of weights to predict the force of the next stone throw from the ripple pattern. Repeatedly performing this cycle of input, interaction and observation is PRC. It reveals patterns over time that can predict chaotic systems, like market trends or the weather, using techniques like linear regression modeling to plot each output as a single point. The magneto-ionic approach. Using this same example above, the “water” in a magneto-ionic PRC is represented by a positive and negative electrode with solid-state electrolyte between them through which ions move when voltage is applied. The application of voltage is equivalent to throwing a stone and the ripple effect is comparable to the movement of oxygen ions in the system. “In addition to its energy efficiency, a useful feature of the magnetoionic system is that time scales for ion diffusion can be controlled from microseconds to minutes,” Atulasimha said. “This leads to simple experimental demonstration, as no megahertz and gigahertz measurements are needed. One can work at the natural time scales of the target application in practical systems and remove the need for complex frequency conversion, which takes both energy and space due to complex electronics.” Atulasimha imagines these energy-efficient reservoir systems have applications in edge computing devices like drones, automated vehicles and surveillance cameras. Tasks such as household energy load forecasting, weather prediction or processing hourly readings from wearable devices, which operate on hour-scale data, can also be performed using magneto-ionic PRC without additional preprocessing. “We showed that the magneto-ionic physical reservoir has both memory and nonlinear behavior, two important properties necessary for using it as a reservoir block,” Rajib said. “Our system stands out because voltage-controlled ion migration is a highly energy efficient method of manipulating magnetization. We demonstrated the required reservoir properties in a physical system and did so using a very energy efficient approach.” Two labs came together in order to pursue this research. Virginia Commonwealth University collaborators included Atulasimha, Rajib, and VCU Ph.D. students Fahim Chowdhury and Shouvik Sarker. The Georgetown University team included Kai Liu, Ph.D., Professor and McDevitt Chair in Physics, Dhritiman Bhattacharya, Ph.D., Christopher Jensen, Ph.D. and Gong Chen, Ph.D. Atulasimha’s group illustrated physical reservoir computing using numerical models of spintronic devices and sought a material system to experimentally demonstrate PRC. Liu’s team worked with magneto-ionic materials and was intrigued by the possibility of using them for computing applications.

The Research Behind the Reputation: TCU’s Dr. Ledbetter Maps the Science of Taylor Swift’s Storytelling

At Texas Christian University, Dr. Andrew Ledbetter, Chair of the Communication Studies Department, is turning his scholarly attention to one of pop culture’s biggest phenomena: Taylor Swift. His research uses data-driven analysis to reveal how Swift’s albums and songs build an interconnected narrative universe — what he calls her “Taylorverse.” Ledbetter ran lyrics across ten albums through semantic-network software to show how certain songs act as linchpins connecting themes of fame, womanhood, love and storytelling. “I was interested in interconnections among the song lyrics,” says Ledbetter. “The songs that are most central have a lot of overlap with other songs, might tend to be songs that are the most popular.”  November 03 0 NBC News The work stands out not just for its pop-culture relevance, but for its academic innovation: combining computational text-analysis with narrative theory to unlock why certain tracks resonate more deeply than others. For journalists, cultural commentators or anyone covering the evolving intersection of music, identity and media, Dr. Ledbetter is a go-to expert. He can speak to how storytelling in music shapes audience engagement, how media fandom becomes scholarship, and why Swift’s songwriting continues to spark new research just as much as chart-topping hits. Andrew Ledbetter is available for interviews - Simply click on his icon now to arrange an interview today.

Australia’s Under-16 Social Media Ban Isn’t a Finish Line - It’s a Reality Check

Australia’s move to restrict social media accounts for kids under 16 has become a global lightning rod - and it’s forcing the right conversation: what do we do when a technology is too powerful for a developing brain? But here’s what I think journalists should focus on next: “A ban is a speed bump, not a seatbelt. It might slow kids down - but it won’t teach them how to drive their attention.” That’s the part that gets lost in the headlines. Because even if you can reduce access, you still have to deal with the why behind the behavior: boredom, social pressure, loneliness, stress, sleep debt. “The headlines make it sound like the problem is solved. But the real question is: what happens in the living room on day three?” Offline.now’s early data shows something important: most people genuinely want to change their screen habits, but many feel overwhelmed and don’t know where to start. That’s why we begin with a quick self-assessment and map people into four Types - Overwhelmed, Ready, Stuck, Unconcerned - so the advice matches the person. “We keep treating social media like a self-control test. It’s not. It’s a confidence problem - people don’t know where to start, so they start with shame.” What I’d tell policymakers considering similar bans 1. Pair friction with skills. “If the only plan is ‘block the app,’ you’re betting against the internet. Workarounds aren’t a bug - they’re the default.” 2. Don’t outsource responsibility entirely to families. “If policy turns parents into full-time bouncers and kids into part-time hackers, we’ve built a system that’s guaranteed to fail.” 3. Ask what gets protected, not just what gets restricted. “The real target isn’t ‘screen time.’ It’s the moments screens replace.” What parents need to know that headlines aren't telling them This is a process, not a switch. The best “first phone / first social” plans are adjustable. Modeling beats monitoring. The rules collapse if adults don’t follow them too. Have a handoff plan. If a child’s mood, sleep, school performance, or withdrawal is deteriorating, it may be bigger than habits. Why this is a late December / January story “The holidays are the perfect storm: more free time, more family friction, more devices, less sleep. January is when the bill comes due.” Journalist angles Bans vs. behavior change: what policy can’t solve The workarounds economy: age gates, bypass culture, privacy tension The four Types: why one-size fits all screen-time advice fails families New Year resets for families: simple, shame-free agreements that stick Available for interviews Eli Singer - CEO of Offline.now; author of Offline.now: A Practical Guide to Healthy Digital Balance. I speak about practical behavior change, non-judgmental family agreements, and confidence-based starting points - and I can direct people to licensed professionals via the Offline.now Directory when needs go beyond coaching.

Changing Phone Habits Isn’t a Willpower Problem. It’s a Confidence Problem.

Every January, millions of people swear they’ll “spend less time on my phone.” By February, they’re right back where they started, only now they feel worse about themselves. Eli Singer, founder and CEO of Offline.now and author of Offline.now: A Practical Guide to Healthy Digital Balance, thinks we’re telling the wrong story. “Most people don’t need another productivity hack or a harsher version of ‘just put your phone down,’” Singer says. “They need one tiny experience that proves, ‘I can actually change this.’ That’s confidence. Without it, willpower doesn’t stand a chance.” Drawing on early data from Offline.now’s self-assessment tool, Singer sees a pattern: people are highly motivated to change, but don’t believe they can stick to anything. His framework sorts users into four Types — Overwhelmed, Ready, Stuck and Unconcerned — based on motivation and confidence. Each Type gets different starting moves, all designed to be done in under 20 minutes. “Telling an overwhelmed parent or burned-out executive to do a 30-day social media fast is like asking someone who’s never run to start with a marathon,” he says. “We focus on micro-wins — one phone-free dinner, ten minutes of swapping doomscrolling for something you actually enjoy — because that’s what rebuilds trust in yourself.” Singer is a coach, not a therapist, but Offline.now’s Digital Wellness Directory connects people with licensed therapists, social workers, coaches and dietitians when deeper clinical support is needed. He positions Offline.now as the “front door” for people who know their relationship with screens isn’t working, but don’t know where to start. Why now January is peak “resolution season” and peak disappointment season. Singer can speak to why traditional “digital detox” narratives don’t work, how confidence and micro-steps change the story, and what a realistic New Year phone reset looks like for real people with jobs, kids and ADHD. Featured Expert Eli Singer – Founder of Offline.now and author of Offline.now: A Practical Guide to Healthy Digital Balance. Singer can speak to the platform’s behavioral data on digital overwhelm, the confidence gap, the Offline.now Matrix, and how 20-minute micro-steps outperform all-or-nothing digital detoxes in the real world. Expert interviews can be arranged through the Offline.now media team.

Study: Lessons learned from 20 years of snakebites

The best way to avoid getting bitten by a venomous snake is to not go looking for one in the first place. Like eating well and exercising to feel better, the avoidance approach is fully backed by science. A new study from University of Florida Health researchers analyzed 20 years of snakebites cases seen at UF Health Shands Hospital in Gainesville. “This is the first time we’ve evaluated two decades of venomous snakebites here,” said senior author and assistant professor of medicine Norman L. Beatty, M.D., FACP. Researchers analyzed 546 de-identified patient records from 2002 to 2022 and highlighted notable conclusions — for instance, that a third of the snakebites analyzed were preventable and caused by people intentionally engaging with wild snakes. “Typically, people’s experiences with getting bitten are due to an interaction that was inadvertent — they stumble upon a snake or reach for something without seeing one camouflaged,” Beatty said. “In this case, people were seeking them out. There were a few individuals who were bitten on more than one occasion.” Most (77.8%) of the snakebites occurred in adult men while they were handling wild snakes, and most of the bites were perpetrated by the diminutive pygmy rattlesnake and the cottonmouth. The latter is named for the white lining of its mouth, which it displays when threatened. “I was less surprised to see those species emerge as some of the most common ones people were bitten by, but the robust presence of other, less common species in the data — like the eastern coral snake, southern copperhead, timber rattlesnake and the eastern diamondback rattlesnake, was interesting,” Beatty said. The eastern diamondback rattlesnake is one of the most venomous snakes in North America. Most patients were bitten on their hands and fingers and around 10% of them attempted outdated self-treatments no longer recommended by doctors — like sucking out the venom. Initially, the study began as a medical student research project, thanks to a handful of medical students who worked with Beatty to review the cases. The intention was to dive deep into the circumstances of each encounter and learn more about the treatment given, as well as the outcomes. Fourth-year medical student River Grace, the paper’s first author, said the work struck a personal note. “My dad is a reptile biologist, so I’ve grown up around snakes my whole life,” Grace said. “He was bitten by a venomous snake many years ago and ended up hospitalized for multiple weeks, so it was interesting to keep that experience in mind while going over the data.” Grace noted that it typically took those bitten over an hour on average to travel from where the bite occurred to the hospital. “It seems like the reason for that was people not knowing exactly what to do once they’d been bitten, or underestimating the severity of the bite,” he said. “Some would just sit at home for hours.” Floridians share their home with a variety of scaly neighbors who don’t always welcome visitors — accidental or not. Ultimately, thanks to the timely care of providers, only three snake bites were fatal. However, antivenom is no panacea. Those who are lucky enough to receive it in time can still incur complications from the original snake bites, like tissue damage, or even a fatal allergic reaction to the antivenom itself. Consequently, researchers look toward improving the processes used to triage snake bites in the emergency room, ensuring that providers are equipped with the knowledge and the know-how to shorten time to treatment. “In the future, we think we’d love to get involved in enhancing provider education so everyone in the health care setting is confident in being able to identify and administer antivenom as quickly and safely as possible,” Grace said.

UF team develops AI tool to make genetic research more comprehensive

University of Florida researchers are addressing a critical gap in medical genetic research — ensuring it better represents and benefits people of all backgrounds. Their work, led by Kiley Graim, Ph.D., an assistant professor in the Department of Computer & Information Science & Engineering, focuses on improving human health by addressing "ancestral bias" in genetic data, a problem that arises when most research is based on data from a single ancestral group. This bias limits advancements in precision medicine, Graim said, and leaves large portions of the global population underserved when it comes to disease treatment and prevention. To solve this, the team developed PhyloFrame, a machine-learning tool that uses artificial intelligence to account for ancestral diversity in genetic data. With funding support from the National Institutes of Health, the goal is to improve how diseases are predicted, diagnosed, and treated for everyone, regardless of their ancestry. A paper describing the PhyloFrame method and how it showed marked improvements in precision medicine outcomes was published Monday in Nature Communications. Graim, a member of the UF Health Cancer Center, said her inspiration to focus on ancestral bias in genomic data evolved from a conversation with a doctor who was frustrated by a study's limited relevance to his diverse patient population. This encounter led her to explore how AI could help bridge the gap in genetic research. “If our training data doesn’t match our real-world data, we have ways to deal with that using machine learning. They’re not perfect, but they can do a lot to address the issue.” —Kiley Graim, Ph.D., an assistant professor in the Department of Computer & Information Science & Engineering and a member of the UF Health Cancer Center “I thought to myself, ‘I can fix that problem,’” said Graim, whose research centers around machine learning and precision medicine and who is trained in population genomics. “If our training data doesn’t match our real-world data, we have ways to deal with that using machine learning. They’re not perfect, but they can do a lot to address the issue.” By leveraging data from population genomics database gnomAD, PhyloFrame integrates massive databases of healthy human genomes with the smaller datasets specific to diseases used to train precision medicine models. The models it creates are better equipped to handle diverse genetic backgrounds. For example, it can predict the differences between subtypes of diseases like breast cancer and suggest the best treatment for each patient, regardless of patient ancestry. Processing such massive amounts of data is no small feat. The team uses UF’s HiPerGator, one of the most powerful supercomputers in the country, to analyze genomic information from millions of people. For each person, that means processing 3 billion base pairs of DNA. “I didn’t think it would work as well as it did,” said Graim, noting that her doctoral student, Leslie Smith, contributed significantly to the study. “What started as a small project using a simple model to demonstrate the impact of incorporating population genomics data has evolved into securing funds to develop more sophisticated models and to refine how populations are defined.” What sets PhyloFrame apart is its ability to ensure predictions remain accurate across populations by considering genetic differences linked to ancestry. This is crucial because most current models are built using data that does not fully represent the world’s population. Much of the existing data comes from research hospitals and patients who trust the health care system. This means populations in small towns or those who distrust medical systems are often left out, making it harder to develop treatments that work well for everyone. She also estimated 97% of the sequenced samples are from people of European ancestry, due, largely, to national and state level funding and priorities, but also due to socioeconomic factors that snowball at different levels – insurance impacts whether people get treated, for example, which impacts how likely they are to be sequenced. “Some other countries, notably China and Japan, have recently been trying to close this gap, and so there is more data from these countries than there had been previously but still nothing like the European data," she said. “Poorer populations are generally excluded entirely.” Thus, diversity in training data is essential, Graim said. "We want these models to work for any patient, not just the ones in our studies," she said. “Having diverse training data makes models better for Europeans, too. Having the population genomics data helps prevent models from overfitting, which means that they'll work better for everyone, including Europeans.” Graim believes tools like PhyloFrame will eventually be used in the clinical setting, replacing traditional models to develop treatment plans tailored to individuals based on their genetic makeup. The team’s next steps include refining PhyloFrame and expanding its applications to more diseases. “My dream is to help advance precision medicine through this kind of machine learning method, so people can get diagnosed early and are treated with what works specifically for them and with the fewest side effects,” she said. “Getting the right treatment to the right person at the right time is what we’re striving for.” Graim’s project received funding from the UF College of Medicine Office of Research’s AI2 Datathon grant award, which is designed to help researchers and clinicians harness AI tools to improve human health.