Pet Humanisation and Related Grief: Development and Validation of a Structured Questionnaire Instrument to Evaluate Grief in People Who Have Lost a Companion Dog
Bibliographic record
Abstract
People often develop strong emotional connections with their dogs and consider them to be members of the family. The purpose of this study was to develop a novel validated tool, the Mourning Dog Questionnaire, to recognise and evaluate the mourning process in people who have lost a dog. The research model was based on a grid of five different questionnaires: the Pet Bereavement Questionnaire, the Lexington Attachment to Pets Scale, the Animal-Human Continuity Scale, the Positivity Scale, and the Testoni Death Representation Scale. The Italian version of the survey was posted on social networks. A sample of 369 Italian dog owners filled in the questionnaire (mean age ± SD 42.00 ± 10.70 years). Reliability indices were good for all instruments. The total scores of the five questionnaires correlated with each other. The results from the Mourning Dog Questionnaire support the negative view of life after the death of a pet and people's tendency to humanise their pet, since dog owners perceived animals no differently from humans in terms of emotions, needs and legal rights. Findings arising from the use of the Mourning Dog Questionnaire will help the implementation of rationality-based strategies to improve the wellbeing, resilience and quality of life of people in the world experiencing the loss of a pet.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".