Family Bonds with Pets and Mental Health during COVID-19 in Australia: A Complex Picture
Bibliographic record
Abstract
The COVID-19 pandemic has drawn attention to the health-promoting features of human-animal relationships, particularly for families with children. Despite this, the World Health Organization’s (1986) Ottawa Charter remains human-centric. Given the reciprocal health impacts of human-animal relationships, this paper aims to (i) describe perceived pet-related benefits, worries, and family activities; and to (ii) examine differences in perceived benefits, worries, and activities for parents and children with and without clinical mental health symptoms. We recruited 1034 Australian parents with a child < 18 years and a cat or dog via a national online survey between July and October 2020. Most parents reported their pet was helpful for their own (78%) and their child’s mental health (80%). Adjusted logistic regression revealed parents with clinical psychological distress were 2.5 times more likely to be worried about their pet’s care, well-being, and behaviour (OR = 2.56, p < 0.001). Clinically anxious children were almost twice as likely to live in a family who engages frequently in pet-related activities (e.g., cooked treats, taught tricks, OR = 1.82, p < 0.01). Mental health and perceived benefits of having a pet were not strongly associated. Data support re-framing the Ottawa Charter to encompass human-animal relationships, which is an often-neglected aspect of a socioecological approach to 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".