Developing and Planning a Protocol for Implementing Health Promoting Animal Assisted Interventions (AAI) in a Tertiary Health Setting
Bibliographic record
Abstract
The Ottawa Charter identifies that multiple levels of government, non-government, community, and other organizations should work together to facilitate health promotion, including in acute settings such as hospitals. We outline a method and protocol to achieve this, namely an Action Research (AR) framework for an Animal Assisted Intervention (AAI) in a tertiary health setting. Dogs Offering Support after Stroke (DOgSS) is an AR study at a major tertiary referral hospital. AAI has been reported to improve mood and quality of life for patients in hospitals. Our project objectives included applying for funding, developing a hospital dog visiting Action Research project, and, subsequent to ethics and governance approvals and finance, undertaking and reporting on the Action Research findings. The Action Research project aimed to investigate whether AAI (dog-visiting) makes a difference to the expressed mood of stroke patients and their informal supports (visiting carers/family/friends), and also the impact these visits have on hospital staff and volunteers, as well as the dog handler and dog involved. We provide our protocol for project management and operations, setting out how the project is conducted from conception to assess human and animal wellbeing and assist subsequent decision-making about introducing dog-visiting to the Stroke Unit. The protocol can be used or adapted by other organizations to try to avoid pitfalls and support health promotion in one of the five important action areas of the Ottawa Charter, namely that of reorienting health services.
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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 | 0.268 | 0.220 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.009 | 0.016 |
| Insufficient payload (model declined to judge) | 0.049 | 0.015 |
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".