Leashes, Litterboxes, and Lifelines: Exploring Volunteer-Based Pet Care Assistance Programs for Older Adults
Bibliographic record
Abstract
At the convergence of population aging and pet-ownership, community stakeholders are well-positioned to support older adults’ relationships with companion animals through age-related transitions in health and living arrangements. In this study’s setting, a volunteer-based pet care assistance program launched in 2017 to provide practical assistance with pet care for socially disadvantaged, community-dwelling older adults. This case study explored the impacts and feasibility of this and similar programs via (i) an Internet-based environmental scan to compare similar programs and (ii) qualitative interviews with a sampling of diverse community stakeholders (n= 9). A small number of comparable international programs (n= 16) were found. Among these, programs were delivered using a range of funding models; fewer than half involved collaborations across human social services and animal welfare sectors; and none addressed all dimensions of support offered by our local program. Analysis of qualitative interviews highlighted five major themes confirming the value of the volunteer-based approach and the importance of cross-sectoral collaborations in addressing older adults’ under-recognized pet care-related needs. Taken together, the findings confirmed the effectiveness of our local program model. Collaborative, cross-sectoral programs that target both human and companion animal well-being hold promise to reduce barriers to pet ownership that many disadvantaged older adults face. This unique approach leverages the health-promoting potential of human-animal relationships in ways that enhance quality of life for individuals, animal welfare, and age-friendliness of communities.
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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 | 0.008 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".