Pets' Impact on Your Patients' Health: Leveraging Benefits and Mitigating Risk
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
Over two thirds of Americans live with pets and consider them important members of the family. Pets benefit human health (zooeyia) in 4 ways: as builders of social capital, as agents of harm reduction, as motivators for healthy behavior change, and as potential participants in treatment plans. Conversely, pets can present risks to their owners. They are potential sources of zoonotic disease and injury. Pets can also challenge a family's prioritization of financial and social resources. To activate the benefits of zooeyia and appropriately calibrate and mitigate zoonotic risk, physicians first need to know about the pets in their patients' families. Asking about pets is a simple and feasible approach to assess patients' environmental history and social capital. Asking about pets is a nonthreatening way to build rapport and demonstrates an interest in the whole family, which can improve the physician-patient therapeutic alliance. Physicians can use an interprofessional, collaborative approach with veterinarians to address zoonotic health risks and leverage zooeyia.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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