Reimagining Healthcare: Human–Animal Bond Support as a Primary, Secondary, and Tertiary Public Health Intervention
Bibliographic record
Abstract
The emergence of human-animal support services (HASS)-services provided to help keep people and their companion animals together-in the United States has been driven by two global public health crises. Despite such impetuses and an increasing recognition of One Health approaches, HASS are generally not recognized as public health interventions. The Ottawa Charter, defining health as well-being and resources for living and calling for cross-sector action to advance such, provides a clear rationale for locating HASS within a public health framework. Drawing from Ottawa Charter tenets and using the United States as a case study, we: (1) recognize and explicate HASS as public health resources for human and animal well-being and (2) delineate examples of HASS within the three-tiered public health intervention framework. HASS examples situated in the three-tier framework reveal a public health continuum for symbiotic well-being and health. Humans and their respective companion animals may need different levels of intervention to optimize mutual well-being. Tenets of the Ottawa Charter provide a clear rationale for recognizing and promoting HASS as One Health public health interventions; doing so enables cross-sector leveraging of resources and offers a symbiotic strategy for human and animal well-being.
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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 | 0.013 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".