Pet ownership and risk of depression: a systematic review and meta-analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| 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) | 0.000 | 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 it