Economics of Intergenerational Volunteering: A Mixed-Methods Study of Snow-Buddies Program in Niagara Region, Canada
Bibliographic record
Abstract
This study examines the financial costs and savings of a community-based intergenerational volunteer program (i.e. Snow-buddies) that pairs youth with older adults for snow removal. From March 2020 to May 2023, Snow-buddies completed 106 volunteer-matches and 486 snow removal events. Using a sequential exploratory mixed-method design, 14 semi-structured interviews were conducted with youth volunteers and older adults. The majority of participants revealed minimal out-of-pocket (OOP) expenses and time/productivity losses for snow removal. Increased mobility, fall prevention, and social connections were perceived benefits of the program. A survey (n = 55, 52% of matched participants) reported an average CAD$123 OOP spending per snow removal event. Applying the rate of fall injuries among older adults due to snow, an estimated 1.12 fall injuries per 486 person-events were prevented translating into a total of $81,398 financial savings from averted hospitalization (i.e. a benefit–cost ratio of ~$662 for every dollar spent on snow removal).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".