MétaCan
Menu
Back to cohort
Record W4361004850 · doi:10.3390/ijerph20075272

Reimagining Healthcare: Human–Animal Bond Support as a Primary, Secondary, and Tertiary Public Health Intervention

2023· article· en· W4361004850 on OpenAlexaboutno aff
Janet Hoy-Gerlach, Lisa Townsend

Bibliographic record

VenueInternational Journal of Environmental Research and Public Health · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsPublic healthCharterIntervention (counseling)Psychological interventionPublic sectorHealth careInternational healthPolitical sciencePublic relationsHealth promotionMedicineGerontologyNursingLaw

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.552

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.454
Teacher spread0.369 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Environmental Research and Public HealthSame topicHuman-Animal Interaction StudiesFrench-language works237,207