CQA-18: 18-Item Compassion Questionnaire for Animals
Bibliographic record
Abstract
The Compassion Questionnaire for Animals (CQA) was developed to measure compassion for animals as a multifaceted construct encompassing affective, cognitive, behavioral, and interrelatedness dimensions, each representing skills that can be cultivated through training and practice. Nonetheless, the original 28-item limited its usability in research. This study aimed to address this limitation by developing a shortened version of the questionnaire while preserving its strengths. The CQA underwent an iterative shortening process that was evaluated in a large-scale validation study was conducted to evaluate the shortened questionnaires. The final version comprised 18 items (CQA-18) with high content and valence balance among items. Psychometric analysis indicated that CQ-18 maintained properties similar to the original questionnaire in terms of internal consistency, convergent validity, and discriminant validity, while also presenting an invariant factor structure by gender. CQA-18 represents a significant reduction in length compared to the original version, while maintaining robust psychometric properties. The study findings underscore the theoretical and practical significance of the questionnaire in assessing and cultivating compassion for animals. However, certain limitations warrant consideration, and the implications for research and clinical practice are thoroughly discussed.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".