Assessing Whether Household Pets Buffer Responses to a Remote Stress Induction
Bibliographic record
Abstract
Interactions with pet animals such as dogs have been shown to have stress-buffering properties, reducing perceived experiences of stress and improving positive ratings of mood and affect. However, experimental evidence has only been demonstrated in novel laboratory environments, where a friendly pet might be a particularly salient stimulus, and to date has largely been restricted to pet dogs. It remains to be seen whether household pets are an effective source of buffering from acute stress within the home environment and whether pet cats may buffer their owners from acute stress. In this study, 191 university students who owned a dog or cat were randomly assigned to interact with them or not, before and after a novel, internet-delivered adaptation of the Trier Social Stress Test (iTSST). Stress responsivity was measured via self-reported stress and anxiety, as well as smartphone-collected photoplethysmography. Observer-coded interactions between owner and pet, owner-reported attitude toward their pet, and species of pet were examined as predictors of stress responsivity. Results indicated that, while interacting with a dog or cat assisted in recovery from the stressor, individuals who interacted with a pet cat demonstrated a blunted response to the iTSST. As well, occurrences of behaviors that were observed during an owner’s interaction with their pet dog or cat were similar before and after the iTSST, suggesting that these behaviors may be an expression of trait-like characteristics. These results suggest that more work is needed on the potential stress-buffering role of interactions with pet cats.
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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) | 0.002 | 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".