Older women’s experiences of companion animal death: impacts on well-being and aging-in-place
Bibliographic record
Abstract
BACKGROUND: Companion animal death is a common source of grief, although the extent and context of that grief is poorly understood, especially in older adulthood. The aim of this multiple-methods study was to develop a greater understanding of the impact of companion animal death on older women living alone in the community, as older women are a distinct at-risk group, and the supports that should be available to help these individuals with their grief. METHODS: Participants were recruited from across Alberta, a Canadian province, through seniors' organizations, pet rescue groups, and social media groups of interest to older women. After completing a pre-interview online questionnaire to gain demographic information and standardized pet attachment and grief measures data, participants were interviewed through the Zoom ® computer program or over the telephone. An interpretive description methodology framed the interviews, with Braun and Clarke's 6-phase analytic method used for thematic analysis of interview data. RESULTS: In 2020, twelve participants completed the pre-interview questionnaires and nine went on to provide interview data for analysis. All were older adult (age 55+) women, living alone in the community, who had experienced the death of a companion animal in 2019. On the standardized measures, participants scored highly on attachment and loss, but low on guilt and anger. The interview data revealed three themes: catastrophic grief and multiple major losses over the death of their companion animal, immediate steps taken for recovery, and longer-term grief and loss recovery. CONCLUSIONS: The findings highlight the importance of acknowledging and addressing companion animal grief to ensure the ongoing well-being and thus the sustained successful aging-in-place of older adult women in the community.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| 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".