Canine Behavioral Assessment and Research Questionnaire (C-BARQ): Validation of the Italian Translation
Bibliographic record
Abstract
The aim of this study is to develop an Italian translation of the 100-item Canine Behavioral Assessment and Research Questionnaire (C-BARQ) version and to validate its psychometric properties, in order to facilitate systematic, large-scale studies on dog behavior for Italian-speaking dog owners. A total number of 803 responses by dog owners were collected online. Using the Principal Axis Method and Common Factor Analysis with Quartimin oblique rotation (p < 0.05), a factorial structure was found including 13 factors composed of 62 items and explaining 53.5% of the total variance. Eight factors showed high reliability (Cronbach’s alpha > 0.70), namely: stranger-directed aggression/fear, dog-directed fear, owner-directed aggression, separation-related behavior, chasing, dog-directed aggression, attachment/attention seeking, and elimination problems. Three factors were slightly under the threshold and two had only modest reliability (non-social fear, energy level, touch sensitivity, excitability and trainability). A potential explanation for factors with low reliability is that the composing items do not describe behaviors resulting from homogeneous stimuli or situations. Although our factorial structure resembled in most respects that of the most recently published Canadian version, some important exceptions are present regarding dog rivalry, intraspecific aggression, fear/aggression towards strangers, touch sensitivity and chewing inappropriate objects. Such differences may be due to demographic and/or cultural differences between the sampled populations. Overall, the results suggest that a 62-item Italian C-BARQ can be reliably used in studies on dog behavior.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".