Understanding the Benefits, Challenges, and the Role of Pet Ownership in the Daily Lives of Community-Dwelling Older Adults: A Case Study
Bibliographic record
Abstract
Human-animal interactions may positively impact the health and well-being of older adults. Considering about one third of community-dwelling older adults report owning a pet, better understanding the benefits, challenges, and the role of pet ownership may help support the relationships between older adults and their pets. This case study aims to better understand the role of pet ownership in the daily lives of older adults and explore the benefits and the challenges of owning a pet for this population. Interviews were conducted with Violet, a 77-year-old dog owner and her healthcare provider. Qualitative data were analyzed by two evaluators and validated by the participants. Both participants agree that the benefits outweigh the challenges for both the older adult and her pet. The benefits and challenges were the following: Violet, taking care of her dog (Jack), (1) could provide Violet with a sense of safety and positively influence her mood; (2) may introduce a slight fall risk; (3) includes financial costs to consider. Ensuring Jack's well-being is important for Violet and her dog benefits from Violet's continual presence and care. The findings suggest that improving the fit between characteristics of the owner and their pet may support the meaningful role of pet ownership within the context of aging-in-place.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".