The Applications and Effects of Emerging Innovative Technology in the Mental Healthcare and Well-Being of Older Adults
Bibliographic record
Abstract
Background: Older adults frequently experience mental health challenges such as stress, loneliness, and depression.Since COVID-19, there has been great interest in technological approaches to improve health outcomes in older adults.This manuscript-based thesis describes three papers related to applications of telehealth, artificial intelligence (AI), and virtual reality (VR) to improve the health of older adults, especially their mental health.Firstly, it aims to lay the groundwork for integrating telehealth and technology applications into psychiatry and healthcare tailored for older adults.Secondly, it introduces a pioneering virtual reality-assisted mindfulness meditation (VR-Mindfulness) program designed for older adults, aiming to investigate its feasibility and efficacy.Methods: Study 1 is a Systematic Review of telehealth for older adults with dementia.Study 2 is a secondary data analysis of a large Canadian database examining whether older adults are comfortable with AI as part of their health care.Study 3 is a randomized controlled trial (RCT, n=22) of a 4-week VR-Mindfulness program 15 minutes/session, 2 times/week vs. waitlist control for older adults (≥ 60 years old) in Montreal, Canada, to investigate changes in mental health outcomes.Results: Study 1: This systematic review found that telemedicine was a feasible and efficacious approach in people with dementia.Study 2: Older adults have an overall positive perception of utilizing AI in different areas of healthcare.Study 3: The RCT demonstrated feasibility, with 75% of the intervention group and 92.8% of the control group completing both assessments.Technological issues were minimal and promptly resolved.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".