ADDRESSING SOCIAL ISOLATION OF OLDER ADULTS: THE COMMUNITY PERSPECTIVE AND CONTRIBUTION
Bibliographic record
Abstract
Abstract This presentation will discuss the Reducing Isolation of Seniors Collective (RISC), a collaboration of three organizations that have implemented nine projects to address social isolation for rural and urban older adults in Saskatchewan, Canada. A survey was completed with the purpose of identifying community-level awareness, knowledge and perspectives on the extent of social isolation of older adults in their communities. Key variables of interest included contributors to social isolation of older adults, barriers to overcoming social isolation, and community efforts and promising assets for addressing social isolation of older adults in their own communities. To develop an understanding of the extent to which respondents are involved with older adults, the survey asked respondents to report how often they supported, observed, advocated for, and/or interacted with seniors. The 271 respondents identified their roles in the community as human service professionals, healthcare professionals, program facilitators, community leaders, organization members, and community volunteers. Three-quarters of respondents reported that they were involved with seniors at least daily or weekly. While 75.3% believed that social isolation of older adults was “somewhat” or “very” common, almost one-half (41.2%) of respondents believed there was not general awareness of social isolation of older adults by other members in their community. Over one-half of the respondents mentioned community programming (55.7%), friends and neighbours (63.1%), and volunteers (57.2%) as community assets that can reduce isolation of older adults. Respondents reported examples of promising interventions in their communities: church support, library programs, transportation service, visiting programs, advocacy groups, and information sessions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".