Development of a discriminative questionnaire to assess nonphysical aspects of quality of life of dogs
Bibliographic record
Abstract
OBJECTIVE: To develop a preliminary discriminative questionnaire for assessment of nonphysical aspects of the quality of life (QOL) of pet dogs and evaluate the questionnaire's content validity, test-retest reliability, and internal consistency. STUDY POPULATION: Owners of 120 dogs. PROCEDURE: Each QOL question had 4 response options, representing descending levels of QOL that were equally weighted. A maximum of 38 items contributed to the QOL score. The questionnaire was administered by telephone to owners of dogs with appointments at a veterinary teaching hospital before the appointment. After the appointment, each dog was classified as sick or healthy by use of defined criteria; owners of healthy dogs had a second interview 3 to 4 weeks later. Test-retest reliability (kappa), internal consistency (Cronbach alpha), and respondents' comprehension were used as criteria for excluding an item. RESULTS: There were 77 sick and 43 healthy dogs. Twenty-two QOL questions had significant kappa values (0.11 to 0.91). The Cronbach alpha values for 5 domains of QOL ranged from 0.45 to 0.61, indicating that the domains had moderate internal consistency (homogeneity). The initial pool of 38 items was reduced to 27. CONCLUSIONS AND CLINICAL RELEVANCE: The questionnaire was designed to complement veterinary assessment of dogs' physical health. The questionnaire may be a useful tool in making decisions regarding dogs' QOL.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".