Today’s teenagers are more digitally connected than ever. The majority have access to smartphones and use social media, and nearly half say they are online almost constantly. To better understand their experiences, Pew Research Center surveyed both teens and parents on a range of screen time-related topics. The questions explored the emotions teens tie to their devices, the impact of smartphones on youth, and the challenges parents face when raising children in the digital age.
The survey of 1,453 U.S. teens ages 13 to 17 and their parents was conducted Sept. 26-Oct. 23, 2023.
Key findings from the survey:
Phone-less: 72% of U.S. teens say they often or sometimes feel peaceful when they don’t have their smartphone; 44% say it makes them feel anxious.
Good for hobbies, less so for socialization: 69% of teens say smartphones make it easier for youth to pursue hobbies and interests; fewer (30%) say it helps people their age learn good social skills.
Parental snooping: Half of parents say they have looked through their teen’s phone.
Smartphone standoffs: About four-in-ten parents and teens report regularly arguing with one another about time spent on their phone.
Distracted parenting: Nearly half of teens (46%) say their parent is at least sometimes distracted by their phone when they’re trying to talk to them.
Teens’ views on screen time and efforts to cut back
How teens feel when they don’t have their phone
Do teens think smartphones are negatively impacting young people?
Teen Views by Gender and Age
Do parents think they spend too much time on their phone?
How often are parents distracted by their phone when talking with their teen?
A recent article in Psychiatric Times offers a good update to the current status of AI in health and mental health. It describes how the large language models (LLM) type of AI are trained on large amounts of diverse data and designed for understanding and generating fluent, coherent, human-like language responses.
Potential of AI and Generative Language Models to Enhance Productivity
LLM’s have the potential to transform a variety of industries including medicine and healthcare. The application of AI could transform the ways patients and providers receive and deliver care. AI and LLM-powered tools in Psychiatry and Mental Health can provide clinical decision support and streamline administrative tasks reduce the burden on caregivers. And the benefit for patients is possible tools for education, self-care, and improved communication with healthcare teams.
What About Accuracy?
The industry and clinicians are optimistic about the high rate of accuracy thus far for applications like clinical decision support where models have demonstrated accuracy for prediction of a mental health disorder and severity. For example, ChatGPT was able to achieve final diagnosis accuracy of 76.9% in findings from a study of 36 clinical vignettes. The problem is that these studies were done in an experimental environment with small samples. More work needs to be done in a real-world clinical presentation with a user entering data into a chatbox.
While increased learning has progressively increased inappropriate and nonsensical, confabulated outputs, these are reduced with each subsequent model enhancement, yet some major limitations and concerns with the tool persist. Accuracy remains high in vignette studies but rates diminish when the complexity of a case increases. One clinical vignette study revealed that “ChatGPT-4 achieved 100% diagnosis accuracy within the top 3 suggested diagnoses for common cases, whereas human medical doctors solved 90% within the top 2 suggestions but did not reach 100% with up to 10 suggestions.”
How to Improve Current Limitations
One way to improve accuracy and higher quality responses is to target learning and fine tune a custom GPT feature allows individual users to tailor the LLM to their specific parameters using plain language prompts. This new feature allows users to input data sets and resources while also telling the custom GPT which references should be used in responses. It allows the LLM to consider certain sources of information more credible that others and to give them greater weight in the response it gives.
Fine-tuning a Customized Learning Process
The Neuro Scholar reference collection includes textbooks and other resources that encompass a wide range of topics in neuroscience, psychiatry, and related fields.
NeuroScholar Custom GPT Inputs and Training Resources included:
Memorable Psychopharmacology by Jonathan Heldt, MD
Goodman & Gilman’s Manual of Pharmacology and Therapeutics
Adams and Victor’s Principles of Neurology, 6th Edition
The Neuroscience of Clinical Psychiatry: The Pathophysiology of Behavior and Mental Illness, Third Edition
The Ninja’s Guide to PRITE 2022 Study Guide, Loma Linda Department of Psychiatry, 15th Edition
Kaplan & Sadock’s Synopsis of Psychiatry, 12th Edition
Lange Q&A Psychiatry, 10thEdition
To test the accuracy of Neuro Scholar, a standardized practice examination for the American Board of Psychiatry and Neurology was selected. Practice examination 1 of Psychiatry Test Preparation and Review Manual, Third Edition consisted of 150 questions. The practice examination was administered to Neuro Scholar and ChatGPT-3.5
Results
ChatGPT-3.5 correctly answered 125 of 150 questions, whereas Neuro Scholar correctly answered 145 of 150 questions, achieving 96.67% accuracy on the practice exam. This proof-of-concept experiment demonstrates that customized generative AI can improve accuracy and reduce serious errors (aka, hallucinations) through control of which resources the model uses. In medicine, AI hallucinations can have disastrous consequences. Efforts to improve AI accuracy must also include efforts to eliminate inaccurate responses. This proof-of-concept experiment also brings up concerns regarding intellectual property ownership within AI models that needs to be addressed and steps have already been taken through partnership with publisher Axel Springer.
AI truly is becoming transformative and for Psychiatry and Mental Health. has made a major leap in progress, as this proof of concept highlights. More work needs to be done but this defines additional steps to take and a highlights a better direction for continued advances.
A recent column in the daily free press reminds us that six years ago Apple introduced new tools built into iOS 12 to “help customers understand and take control of the time they spend interacting with their iOS devices.” These new features called Screen Time included Activity Reports, App Limits and new Do Not Disturb and Notifications controls.
Photo via Apple
The goal was to offer users detailed information and tools that would help them better understand and control the time they spend with apps and websites, how often they pick up their iPhone or iPad during the day and how they receive notifications.
Ten years earlier Apple had introduced parental controls for iPhone and over that time the developers have worked to add features to help parents manage their children’s content. With Screen Time, these new tools are empowering users who want help managing their device time, and balancing the many things that are important.
So what happened? Did no one use it?
Consumers rejoiced that Apple offered a solution to the issues of screen use. Unfortunately Apple presented us with a useful tool, not a solution.
Today we are still struggling with the negative effects that too much screen time has on our physical, emotional and mental health. A research study recently published in The Journal of Mood & Anxiety Disorders, found that that one-third of youth (aged between 10 and 24 years) spend four or more hours a day engaging with their screens. The effects of such are great, leading to health and mental health problems, among other things. In teenagers, screen media activity (SMA) consumes up to 60% of their after-school time and nearly 97% of US youth have at least one electronic item in their bedroom.
Not An Individual Activity But A Complex and Multifaceted Problem
SMA in youth is often perceived as an individual activity. The authors point out that the relationship between SMA and mental health outcomes in youth is a complex and multifaceted issue that has garnered significant attention among researchers and the public in recent years. The complexity may be due to the diverse nature of screen activities, the rapidly evolving landscape of digital media , and the differential impacts these activities may have across individuals. What is emerging is a nuanced picture, with some evidence suggesting relatively trivial effects of SMA on well-being or life satisfaction and other results indicating stronger associations with mental health problems such as depression or anxiety. More recent research suggests there may be individual differences concerning the impact of SMA. For example, sex-related differences have emerged, with girls generally demonstrating stronger associations between screen media time and mental health indicators than boys; and there is some evidence that effects may differ depending on the broader socioeconomic and environmental context. The COVID-19 pandemic added complexity, influencing screen time habits and mental health outcomes.
An Overeview of the Complexites
To better understand and research SMA, it is more accurate to view it as occurring within a system that encompasses the individual, the immediate caregiver environment, the school, peers and other environmental factors. The authors utilize the Bronfenbrenner’s ecological systems theory, which is a framework for understanding human behavior within a complex system of relationships within and across multiple levels of the environment, from more proximal (e.g., immediate family, academic settings) to more distal (e.g., sociocultural values, laws, etc.). The theory proposes five interrelated systems that influence development:
(1) the microsystem which is the immediate environment with which an individual interacts,
(2) the mesosystem which focuses on interactions between different elements of the microsystem,
(3) the exosystem which involves the larger social system with which the individual does not directly interact but it still impacts their behavior,
(4) the macrosystem comprised of the broader societal and cultural context, and
(5) the chronosystem that is centered on the dimension of time including the timing of specific events and historical context.
Figure 1 shows the Bronfenbrenner model conceptual overview of screen media activity and mental health.
Figure 1
Both clinicians and researchers could benefit from reading this article. It provides an excellent birdseye view of the multifaceted interrelationships that can be an important part of screen media activity in youth today and applicable to society in general in today’s tech-obsessed world.
