Pet ownership and risk of depression: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Pet ownership is often believed to confer psychological benefits, such as reducing loneliness and providing emotional support. However, evidence on its relationship with depression is mixed, and no clear consensus currently exists. This systematic review and meta-analysis aimed to evaluate the association between pet ownership and the risk of depression. METHODS: A comprehensive systematic review and meta-analysis were performed following PRISMA guidelines. Three electronic databases (PubMed, Scopus, Web of Science) were searched for observational studies assessing the impact of pet ownership on depression. Two independent reviewers screened and extracted data, and study quality was evaluated using the Newcastle-Ottawa Scale. Random-effects models were used to compute pooled odds ratios (ORs) and 95% confidence intervals (CIs) using STATA-17. RESULTS: A total of 21 studies involving 159,322 participants were included. Overall, pet ownership was not associated with a significant change in depression risk compared to non-ownership (OR: 1.03; 95% CI: 0.995-1.07). However, sensitivity analyses by pet type revealed that cat ownership was associated with a modestly increased risk of depression (OR: 1.06; 95% CI: 1.02-1.09), whereas dog ownership showed no significant association (OR: 0.93; 95% CI: 0.789-1.10). CONCLUSION: This study reveals a complex relationship between pet ownership and depression. Cat ownership is linked to a higher risk, while dog ownership shows mixed results. Overall, pet ownership isn't significantly associated with depression, highlighting the need for further research into its psychosocial dynamics and mental health implications.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.032 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".