Rural Aging during COVID-19: A Case Study of Older Voluntarism
Bibliographic record
Abstract
Abstract During large-scale crises such as the COVID-19 pandemic, the precarity of older people and older volunteers can become exacerbated, especially in under-serviced rural regions and small towns. To understand how the pandemic has affected “older voluntarism”, this article presents a case study of three volunteer-based programs in rural Ontario, Canada. Interviews with 34 volunteers and administrators reveal both challenging and growth-oriented experiences of volunteers and the programs during the first wave of COVID-19. The findings demonstrate the vulnerability and resiliency of older volunteers and the adaptability and uncertainty of programs that rely on older voluntarism, as the community and its older residents navigate pandemic-related changes. The article advances a framework for understanding the pandemic’s impacts on older voluntarism in relation to personal, program, and community dimensions of sustainable rural aging. Further, it explores ways that older volunteers, organizations that depend on them, and communities experiencing population aging can persevere post-pandemic.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".