Artificial Intelligence in Healthcare: Perceptions among Older Adults in Northern Ontario, Canada
Bibliographic record
Abstract
BACKGROUND: Globally, the size and proportion of older adults are growing at an unprecedented rate. Given the rising incidence of health complications among older adults, it is critical to understand opportunities for improving the delivery of health care. Artificial intelligence (AI) is a promising technology that many argue has the potential to improve patient care, particularly by supporting and assisting older adults in diagnosing, managing, and treating health conditions. Although there have been significant studies on AI innovations in health care, there is an overall lack of literature on patient perceptions of the implementation of AI, specifically in older adult populations. This study addresses this gap in research by engaging older adults and focusing on AI and healthy aging. METHOD: Utilizing an appreciative inquiry framework, the data collection consists of interviews with older adults to develop an understanding of how older adults perceive the use and consideration of AI in Northern Ontario, Canada. We analyze the findings using Braun and Clark's (2022) thematic analysis six-step approach. RESULT: The findings from this study highlight how AI technologies can better support older adults in Northern Ontario. The study highlights identifying older adults' perceptions toward AI in healthcare settings, as well as the perceived barriers and facilitators to AI utilization in Adult Day Centers/Older Adults Centers and Retirement Homes. The study contributes to the development of specialized educational materials or training programs designed to raise awareness and practice, encourage advocacy, improve comfort levels with AI technologies, and ultimately enhance older adult patient care. The findings will be informed to policymakers and stakeholders for adjusting policy reform and analysis. This study shed light on how older adults feel toward AI, how they envision improvements, and what improvements could be made for future health practices using AI technologies. CONCLUSION: Overall, this research could help identify factors contributing to better AI-enabled care of older adults, and it could shed light on how AI technologies could evolve to better meet the needs of older adults.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".