Preparing for Assistance Dog Retirement: What do we currently know about the retirement of assistance dogs?
Bibliographic record
Abstract
Assistance dogs significantly support individuals facing physical, mental health, and well-being challenges, providing both functional aid and emotional support. Despite their clear benefits and increasing demand in the UK, understanding and research on the retirement phase of assistance dog partnerships are limited. This scoping review aims to explore this knowledge gap by evaluating existing literature on assistance dog retirement and identifying key patterns, themes, and gaps. Following Arksey and O'Malley's framework, the review specified the research question, identified relevant literature, selected studies, charted the data, and summarised, synthesised, and reported results. The search focused on published journal articles from seven databases, retrieving three hundred and ninety-four initial results reviewed independently by two research team members. After screening, fourteen final papers were examined, showing growing subject interest from 2016 onwards, with contributions primarily from Western countries including the USA, UK, Australia, Canada, and New Zealand. Research commonly explored three overarching themes: the impact of retirement on the human/user, on the assistance dog, and the retirement process itself, with sub-themes such as the emotional impact of loss, grief, partnership success, animal welfare, and retirement implications. Preliminary findings suggest an international gap in preparing owners and families for assistance dog retirement and a lack of standardised guidelines and policies. Further research should develop support mechanisms for loss and retirement, expand the diversity of studied populations, and establish standardised retirement guidelines. Addressing these areas is crucial for enhancing the well-being of both assistance dogs and their users in preparation for and throughout the retirement process.
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 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 | 0.016 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".