Understanding the Role of Therapy Dogs in Human Health Promotion
Bibliographic record
Abstract
Dogs may provide humans with a range of physical, mental and social benefits. Whilst there is growing scientific evidence of benefits to humans, there has been less focus on the impact to canine health, welfare and ethical considerations for the dogs. The importance of animal welfare is increasingly acknowledged, indicating that the Ottawa Charter should be extended to include the welfare of non-human animals supporting the promotion of human health. Therapy dog programmes are delivered across a variety of settings including hospitals, aged care facilities and mental health services, highlighting the important role they play in human health outcomes. Research has shown that that there are biomarkers for stress in humans and other animals engaged in human-animal interactions. This review aims to assess the impact of human-animal interactions on therapy dogs engaged in providing support to human health. While challenging, it is paramount to ensure that, within the framework of One Welfare, the welfare of therapy dogs is included, as it is a key factor for future sustainability. We identified a range of concerns due to the lack of guidelines and standards to protect the wellbeing of the dogs engaged in these programmes. Extension of the Ottawa Charter to include the welfare of non-human animals with leveraging through a One Welfare approach would promote animal and human health beyond current boundaries.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".