Beyond “Doing Better”: Ordinal Rating Scales to Monitor Behavioural Indicators of Well-Being in Cats
Bibliographic record
Abstract
Safeguarding the well-being of cats is essential to the mission of any responsible animal shelter. Environmental enrichment and behaviour modification are often key to this goal. Measuring response to these interventions is essential to ensure strategies are successful. There are often many staff and volunteers involved in these efforts, and a lack of standardised language can make monitoring progress difficult. Ordinal rating scales of key behaviours can be a useful way to summarise observations and ensure that common language is used. However, it is crucial that these scales have good interobserver agreement and reliability, so operational definitions and training systems are important. This paper presents a method for evaluating four different expressions of cat welfare on a 0-5 scale: modified Fear, Anxiety, and Stress score; Response to Petting score; Participation in Play score; and Food Intake Summary score. All scales showed almost perfect average interobserver agreement (linear weighted κ) and excellent average interobserver reliability (interclass correlation coefficient). These scales may prove useful to other shelters, or any other environment where evaluating response to interventions is important to the welfare of cats, such as research facilities or home environments. The exceptional interobserver agreement and reliability of this study compared with some others highlights the importance of standardised training programs.
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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 | 0.019 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".