Biopsychosocial Factors and Cognitive Function in Cat Ownership and Attachment in Community-dwelling Older Adults
Bibliographic record
Abstract
Few studies consider the health benefits of pet ownership from a biopsychosocial perspective, and a paucity of studies investigate cat ownership. The current study was designed to determine if psychosocial factors (stress, loneliness, and depression), biological levels of stress and inflammation (salivary cortisol, interleukin-1β, and C-reactive protein [CRP]), and cognitive function were associated with companion cat ownership/attachment in community-dwelling older adults. Community-dwelling older adults (n = 96, mean age = 76.6 years) who either owned a cat and no dog (n = 41) or owned neither a cat nor a dog (n = 55) completed questionnaires (Perceived Stress Scale, Revised–UCLA Loneliness Scale, Geriatric Depression Scale-Short Form, Montreal Cognitive Assessment, and Lexington Attachment to Pets Scale) and provided saliva specimens which were assayed for stress and inflammatory biomarkers. The majority of participants screened positive for mild cognitive impairment, reported low levels of stress, loneliness, and depression, and the biomarkers reflected fairly low levels of stress and inflammation. Binary logistic regression analysis revealed that psychosocial factors, salivary biomarkers, and cognitive function were not significantly associated with cat ownership. Age was the only significant predictor of cat ownership (OR = 0.92, p < 0.01) with the odds of cat ownership decreasing by 8.3% per year of advancing age. On average, cat owners were “somewhat attached” to their cats; however, 26% were “strongly attached” to their cats. Correlation analyses revealed the level of attachment to cats was not associated with study outcomes. These results show that cat ownership declined with each advancing year, which lessens the opportunity for older adults to form attachment bonds. The level of pet attachment supports the consideration of cats as a source of an attachment relationship for older adults, including those with cognitive impairment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".