Grief Resulting from Euthanasia and Natural Death of Companion Animals
Bibliographic record
Abstract
Pet death, like other losses, requires that the bereaved adjust to the often severe consequences of that loss. Previous research suggests there may be specific owner characteristics and situational variables that can affect how individuals adjust to the loss of their pet (Thomas, 1982). The present study investigated the influence of a number of variables on how one adjusts to companion-animal death including: Cause-of-Death (euthanasia versus natural death); Attachment; Gender; Age; Time-Since-Loss; Type-of-Pet; Replacement-of-Pet; and Household-Make-up. Voluntary participants ( N=103) completed the Grief Experience Inventory (Sanders, Mauger,&Strong, 1985), the Companion Animal Loss Scale (Stallones, Johnson, Garrity,&Marx, 1989), and a General Information Questionnaire. Major findings indicated that: 1) owners whose pets died naturally experienced significantly more total grief, social isolation, and loss of control compared to owners who had their pets euthanized; 2) female owners experienced significantly greater depersonalization, death anxiety, and rumination compared to males; 3) younger owners experienced significantly greater anger/hostility and despair than elderly owners; and 4) owners who lived alone experienced significantly greater somatization than owners who lived with others. Results of the present study suggest reasons why some owners may be “at risk” for excessive grief reactions due to the loss of their companion animal. The importance of providing bereaved owners with a source of mental health counseling is discussed, and directions for future research are suggested.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".