Predictors of owner response to companion animal death in 177 clients from 14 practices in Ontario
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of grief and client desires and needs as they relate to pet death. DESIGN: Cross-sectional mail survey. SAMPLE POPULATION: 177 clients, from 14 randomly selected veterinary practices, whose cat or dog died between 6 and 43 days prior to returning the completed questionnaire. PROCEDURE: Veterinary practices were contacted weekly to obtain the names of clients whose pets had died until approximately 200 clients were identified. Clients were contacted by telephone, and a questionnaire designed to measure grief associated with pet death was mailed to those willing to participate within 1 to 14 days of their pet's death. The questionnaire measured potential correlates and modifiers of grief and included three outcome measures: social/emotional and physical consequences, thought processes, and despair. Demographic data were also collected. RESULTS: Approximately 30% of participants experienced severe grief. The most prominent risk factors for grief included level of attachment, euthanasia, societal attitudes toward pet death, and professional support from the veterinary team. CONCLUSIONS AND CLINICAL RELEVANCE: Bivariate and multivariate analyses highlighted the impact owners' attitudes about euthanasia and professional intervention by the veterinary team had on reactions to pet death. Owners' perceptions of societal attitudes, also a predictor of grief, indicate that grief for pets is different than grief associated with other losses.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".