Best Practices and Policies for Addressing Social Isolation Among Older Adults
Bibliographic record
Abstract
Abstract Isolation has been flagged as a major health and social problem for seniors. Yet, many seniors themselves, their friends/family, and carers for seniors may not recognize risk factors for isolation or know what to do if a senior is isolated. Results from the 2016 General Social Survey noted that 27% of seniors reported they were not socially connected with others, with 20% reporting that they lacked support to carry out chores, and 17% reported feeling isolated. However, there has yet to be a comprehensive review of the evidence to suggest what has emerged as best practices and key policy enablers. To address this, seniors and other key stakeholders (n=200) in the community were interviewed on their perspectives and experiences of social isolation. Additionally, three focus groups (n=24) were conducted, along with a consensus meeting, to identify top priorities, best practices and develop implementation strategies. The priority areas identified were: 1) opportunities for seniors to network and be part of the social fabric; 2) initiatives promoting inclusive community development; 3) programs that promote education related to social isolation; 4) develop services that place an emphasis on partnerships/collaborations; 5) services that are sustainable over the longer term. By mapping the best, emerging practices and policies for social isolation, the ability to synthesize the evidence on social isolation and co-create knowledge translation tools with seniors and other stakeholders will be possible. This will help identify solutions and policies that can be used by governments, health systems, and individuals to comprehensively target social isolation.
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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 | 0.076 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".