Prioritizing Technology Initiatives to Reduce Social Isolation and Loneliness Among Older Adults
Bibliographic record
Abstract
In their insightful commentary, Kokorelias et al. (2024) explore the potential of technology in supporting aging in the right place, addressing both opportunities and challenges from individual to societal levels. Our commentary specifically focuses on recent empirical evidence for technology's benefits in enhancing social connectivity and reducing loneliness for older adults, both with and without cognitive impairments. It emphasizes the need for a proper balance between the use of technology and face-to-face interactions and highlights the importance of addressing concerns related to privacy, cybersecurity and safety in this domain. In addition to the barriers outlined by Kokorelias et al. (2024), we discuss challenges related to the transfer of technology, the necessary steps required to ensure that technological interventions are effective beyond well-controlled studies and the responsibility of industries to design technology in such a way that innovations can benefit as many people as possible.
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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 | 0.016 | 0.137 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.037 | 0.028 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".