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Record W4396937863 · doi:10.1079/hai.2024.0012

SAFE – A risk management tool to protect both people and pets in residential aged care facilities

2024· article· en· W4396937863 on OpenAlexaboutno aff
Lisel O’Dwyer, Janette Young

Bibliographic record

VenueHuman-Animal Interactions · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessResidential careEnvironmental healthRisk managementMedicineNursingFinance

Abstract

fetched live from OpenAlex

Abstract Pet ownership has known health and well-being benefits for people of all ages. Most previous research on pet ownership among older people has focussed on older people with pets living independently in their own homes or the effects of visiting pet programs in residential aged care. With structural ageing of populations globally, the number of humans living into old age is increasing. Even with home support and care policies, an increasing number will need to live in communal aged care settings. Currently, pets rarely accompany older persons into communal residential aged care. This article presents a risk management tool – Safe Animal Friendly Environments (SAFE) – designed to facilitate and maintain private pet ownership among older people living in residential aged care facilities. SAFE was developed to identify best practice for both human and animal well-being in residential aged care. The tool supports both human and animal well-being during a human stage of life with many losses and pains while reducing the number of pet animals needlessly relinquished or even euthanised when owners need to ‘go into care’. It was developed using a Delphi process with multidisciplinary expert input. We identify the different types of risks for stakeholders (residents with pets, aged care facility staff and pets), including physical, zoonotic and psychological risks. None of the identified risks of pets in aged care are unmanageable. SAFE reduces risks to acceptable levels and directs how to remove them where possible. SAFE has a summative table listing 17 general risks: from animals to humans, humans to animals and animals to animals. Each identified risk has a pre-mitigation risk assessment (low, medium or high), recommended mitigation actions and a post-mitigation risk rating (low, medium or high). Post-mitigation risk is reduced to ‘low’ in almost all scenarios. SAFE has separate tables for dogs, cats, small mammals, birds and fish, each preceded by a best practice case study. The discussion links the Ottawa Charter for (human) health promotion and use of SAFE. SAFE contributes to the inclusion of residential aged care as a context for the personal human-animal bond.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.038
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.038
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.001
Science and technology studies0.0020.002
Scholarly communication0.0040.004
Open science0.0020.008
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0120.003

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.

Opus teacher head0.018
GPT teacher head0.350
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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