Older adults and social support in rural Canada: A rapid mixed methods systematic review to inform social prescribing
Bibliographic record
Abstract
OBJECTIVES: Social support may assist people's health as they age but less is known about how this relationship may differ for older adults living in rural communities. Knowledge of this information can inform the implementation of social prescribing, a care model aiming to address people's unmet non-medical social needs. METHODS: This was a systematic rapid mixed-methods review following guidelines. We searched 10 electronic sources (all languages from 2000 and later) for peer-reviewed studies; our last search was on May 5, 2025. We followed the Joanna Briggs Institute (JBI) mixed-methods approach and used a convergent integrated method to create qualitative findings from quantitative studies and merged them with data from qualitative studies. SYNTHESIS: We included 12 studies (14 publications) with six quantitative studies, five qualitative studies, and one mixed methods study. Data were from Canada-wide surveys, or the provinces of New Brunswick, Ontario, Quebec, and Saskatchewan. There were some differences in findings between older adults from rural and urban settings for social support and satisfaction. Older people in rural settings may have less access to "formal" support and may rely more on family or friends, but this "patchwork" of support in rural communities may be less sustainable. CONCLUSION: Social support is an important part of aging, but there may be some unique differences for people living in rural Canadian communities. Although the support provided in rural settings may offer some advantages, it may also be precarious in the long term and innovations to support aging in place (like social prescribing) are long overdue. SYSTEMATIC REVIEW REGISTRATION: PROSPERO 2024 CRD42024591884.
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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 | 0.052 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".