INNOVATIVE APPROACHES TO ADDRESSING LONELINESS AND PROMOTING AGING IN COMMUNITY
Bibliographic record
Abstract
Abstract Research has shown that older adults who are lonely and socially isolated are at increased risk for various negative health outcomes. This symposium brings together leading experts across the globe in the fields of gerontology, public health, and social work to share innovative approaches to addressing loneliness and fostering connections. The symposium will begin with Dr. Ma from the National Singapore University of Social Sciences (SUSS) to share the voice of seniors on digital inclusion for a healthy community in Singapore. Dr. Shapira from Ben-Gurion University of the Negev, Israel, and her research team will discuss a 7-session digital intervention to promote effective coping and alleviate depression and loneliness among older adults in Israel. The symposium will then examine community-based efforts, with Dr. Hou from The University of Central Florida (UCF) in the United States discussing the role of healthcare providers in screening and identifying loneliness and examining communication and comfortability in conducting loneliness screening among older patients. Lastly, Dr. Kuo from Chung-Shan Medical University (CSMU) will introduce the five domains of living a good life and how older adults responded to these topics to prepare themselves for better aging in the community. The presenters will discuss strategies for fostering community-based support networks to address community-dwelling older adults’ physical, social, and emotional needs. This global symposium will also examine the role of technology in supporting community-based efforts, including using virtual communication platforms, online resources, and digital tools to promote community engagement and connection. This is an International Comparisons of Healthy Aging Interest Group Sponsored Symposium. This is a collaborative symposium between the Aging Among Asians and International Comparisons of Healthy Aging Interest Groups.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".