One Health: Fostering Hope for Older Adults and Homeless Companion Animals
Bibliographic record
Abstract
The One Health model proposes that human and nonhuman animal health be addressed in tandem, considering the well-being of both, and even including the environment. However, in practice One Health initiatives usually focus on animals as disease carriers. This paper is innovative because it extends the application of the One Health model to human and nonhuman animal well-being and mental health. One of the most challenging issues in non-human animal welfare is the management of unwanted, abandoned, and feral animals. Many of these animals will end up in a shelter or a rescue, and whether they leave alive is often a reflection of their behavior in the shelter/rescue. Research reviewed here demonstrates that innovative programs in shelters, such as foster programs or standardized training to enable volunteers to assist shelter animals to engage in behavior modification, increase the likelihood of nonhuman animals leaving a shelter alive. The more safe and expertly guided socialization opportunities these nonhuman animals have, the better their chances are of finding a permanent home. Older adults with a lifetime of experience caring for nonhuman animals are an untapped resource for shelters/rescues. Given the well-established research that documents the positive influence of nonhuman companions on human health and well-being, it is suggested here that recruiting older adults to volunteer and/or foster shelter animals would create better outcomes for both groups. By expanding One Health initiatives to include those that enhance the well-being of both human and nonhuman animals, there is potential for a positive impact on physical, mental, and survival outcomes.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".