An Examination of the Potential Role of Pet Ownership, Human Social Support and Pet Attachment in the Psychological Health of Individuals Living Alone
Bibliographic record
Abstract
While researchers have examined the relationship between pet ownership and psychological health among individuals in the general population, the few studies that have examined the possible psychological health benefits of pet ownership for individuals living alone have primarily been conducted among subgroups such as seniors. Using a community sample of adults who were living alone, we hypothesized that pet ownership (pet vs. no pet), emotional attachment levels to pets, and human social support would interact to predict scores on measures of loneliness and depression. A sample of 132 Canadian dog and cat owners as well as non-owners who lived alone completed an on-line survey containing measures of human social support, emotional attachment to pets, loneliness, and depression. Results revealed that neither pet ownership nor attachment to pets predicted the loneliness or depression levels of individuals living alone. However, when we examined the interaction of pet ownership and human social support in the prediction of psychological health, simple effects revealed that dog owners with high levels of human social support were significantly less lonely than non-owners. Furthermore, when we examined the interaction of attachment and human social support in the prediction of psychological health, simple effects revealed that among pet owners with low levels of human social support, high attachment to pets predicted significantly higher scores on loneliness and depression. These findings highlight the complex nature of the relationship between pet ownership and psychological health.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".