Asking About Pets Enhances Patient Communication and Care: A Pilot Study
Bibliographic record
Abstract
This research explored whether asking patients about their pets would enable better environmental/social history taking, and improve patient communication/care. Primary health care providers (PHPs) were surveyed about prevalence of patients living with pets, the health impact of pets, and influences on patient communication. Following an educational intervention, they committed to asking patients about their pets. A follow-up survey was conducted electronically. PHPs were recruited at a continuing medical education (CME) conference and at CME workshops. All 225 participants were PHPs. At the conference, participants were educated one-on-one about the clinical relevance of pets in the family. CME sessions were large or small group teaching. Baseline and final surveys measured awareness of pets in patients' families, assessment of determinants of health, impact on rapport with patients, and patient care. A sign test assessed difference in scores using repeated-measures analysis. Binomial outcomes were assessed using Fisher's exact test. Comments were themed. Ninety-four PHPs (42%) completed the study. Pet-related discussions opened communication with patients. Two-thirds of participants identified positive effects on practice and on relationships with patients. PHPs were able to leverage the health benefits of pets (zooeyia) and mitigate zoonotic risk. Asking patients about pets in the family reveals clinically relevant information, improves communication, and strengthens the therapeutic alliance.
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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 | 0.008 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".