2022 ISFM/AAFP Cat Friendly Veterinary Environment Guidelines
Bibliographic record
Abstract
PRACTICAL RELEVANCE: The '2022 ISFM/AAFP Cat Friendly Veterinary Environment Guidelines' (hereafter the 'Cat Friendly Veterinary Environment Guidelines') describe how the veterinary clinic environment can be manipulated to minimise feline patient distress. Many components of a veterinary clinic visit or stay may result in negative experiences for cats. However, much can be done to improve a cat's experience by making the veterinary clinic more cat friendly. Exposure to other cats and other species can be reduced, and adjustments made with consideration of the feline senses and species-specific behaviour. Caregivers can prepare cats for a clinic visit with appropriate advice. Waiting rooms, examination rooms, hospital wards and other clinic areas can be designed and altered to reduce stress and hence encourage positive emotions. Changes need not be structural or expensive in order to be effective and make a difference to the cats and, in turn, to cat caregivers and the veterinary team. Moreover, by improving the all-round experience at the veterinary clinic, there are positive effects on preventive healthcare, identification of and recovery from illness, and compliance with treatment. CLINICAL CHALLENGES: Good feline healthcare necessitates visiting the veterinary clinic, which, simply by being outside of a cat's territory and familiar surroundings, may lead to negative experiences. Such experiences can trigger negative (protective) emotions and associated physiological stress, which can result in misleading clinical findings, patient distress, prolonged recovery from illness, further difficulties with handling at subsequent visits and potential veterinary personnel injury. There may be a mistaken belief that veterinary clinics must undergo significant renovation or building work to become cat friendly, and that, if species cannot be separated, then clinics cannot improve their care of cats. These Guidelines aim to dispel any such misconceptions and provide detailed practical advice. EVIDENCE BASE: These Guidelines have been created by a Task Force of experts convened by the International Society of Feline Medicine and American Association of Feline Practitioners, based on an extensive literature review and, where evidence is lacking, the authors' experience. Endorsements: These Guidelines have been endorsed by a number of groups and organisations, as detailed on page 1161 and at icatcare.org/cat-friendly-guidelines and catvets.com/environment.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.014 | 0.004 |
| Insufficient payload (model declined to judge) | 0.109 | 0.070 |
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".