2022 AAFP/ISFM Cat Friendly Veterinary Interaction Guidelines: Approach and Handling Techniques
Bibliographic record
Abstract
PRACTICAL RELEVANCE: The '2022 AAFP/ISFM Cat Friendly Veterinary Interaction Guidelines: Approach and Handling Techniques' (hereafter the 'Cat Friendly Veterinary Interaction Guidelines') support veterinary professionals with feline interactions and handling to reduce the impact of fear and other protective (negative) emotions, in so doing enhancing feline welfare and In implementing these Guidelines, team satisfaction and cat caregiver confidence in the veterinary team will increase as the result of efficient examinations, better experience, more reliable diagnostic testing and improved feline wellbeing. Veterinary professionals will learn the importance of understanding and appropriately responding to the current emotional state of the cat and tailoring each visit to the individual. CLINICAL CHALLENGES: Cats have evolved with emotions and behaviors that are necessary for their survival as both a predator and prey species. A clinical setting and the required examinations and procedures to meet their physical health needs can result in behavioral responses to protective emotions. Cat friendly interactions require understanding, interpreting and appropriately responding to cats' emotional states and giving them a perceived sense of control while performing the required assessment. EVIDENCE BASE: These Guidelines have been created by a Task Force of experts convened by the American Association of Feline Practitioners and the International Society of Feline Medicine, 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 organizations, as detailed on page 1127 and at catvets.com/interactions and icatcare.org/cat-friendly-guidelines.
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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 | 0.009 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.016 | 0.004 |
| Insufficient payload (model declined to judge) | 0.092 | 0.069 |
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".