AI Interventions: Assessing the Potential for a ChatGPT Based Loneliness and Cognitive Decline Intervention in Geriatric Populations
Bibliographic record
Abstract
As life expectancy continues to rise, the prevalence of age-associated neurodegenerative diseases like Alzheimer's is increasing, highlighting the urgent need for effective interventions to combat cognitive decline and loneliness among seniors. Previous research has found significant links between social isolation and cognitive decline, with increased social isolation associated with faster cognitive decline. Additionally, social isolation significantly increases the risk of cognitive decline and dementia. Older adults are particularly vulnerable to social isolation due to factors such as the loss of close relationships, retirement, and mobility issues. Certain subpopulations, like older foreign-born adults and LGBTQ+ seniors, face heightened risks of loneliness and social isolation. Addressing these issues requires tailored interventions that can provide meaningful social engagement and support. Current interventions include methods such as psychotherapy, community-based exercise programs, group therapies, social prescription programs, and home-based interventions which play significant roles in enhancing social connectedness and mental well-being in senior populations. Recent technological advancements have introduced new avenues for addressing loneliness and cognitive decline in seniors. Artificial intelligence (AI), particularly conversational agents like GPT-4o, offers significant potential for creating personalised and accessible interventions for seniors. GPT-4o's ability to understand and generate human-like text makes it a valuable tool for providing companionship, cognitive training, and emotional support. The current paper aims to explore the potential for a GPT-4o chatbot intervention in optimising care for isolated seniors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".