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Record W4394757960 · doi:10.1186/s40249-024-01198-0

Towards an actionable One Health approach

2024· article· en· W4394757960 on OpenAlexaff
Xiaoxi Zhang, Zohar Lederman, Lefei Han, Janna M. Schurer, Li-hua Xiao, Zhibing Zhang, Qiulan Chen, Dirk U. Pfeiffer, Michael P. Ward, Banchob Sripa, Sarah Gabriël, Kuldeep Dhama, Krishna Prasad Acharya, Lucy J. Robertson, Sharon L. Deem, Cécile Aenishaenslin, Filipe Dantas‐Torres, Domenico Otranto, Delia Grace, Yang Wang, Peng Li, Chao Fu, Patrícia Poeta, Md. Tanvir Rahman, Kokouvi Kassegne, Yongzhang Zhu, Kun Yin, Jiming Liu, Zhaojun Wang, Xiaokui Guo, Wenfeng Gong, Bernhard Schwartländer, Minghui Ren, Xiao‐Nong Zhou

Bibliographic record

VenueInfectious Diseases of Poverty · 2024
Typearticle
Languageen
FieldMedicine
TopicZoonotic diseases and public health
Canadian institutionsUniversité de MontréalCentre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal
FundersWorld Health OrganizationBill and Melinda Gates Foundation
KeywordsMultidisciplinary approachContext (archaeology)Public relationsAction (physics)Psychological interventionCapacity buildingCommissionPublic healthGlobal healthLegislatureMedicinePolitical scienceEngineering ethicsEngineeringNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Despite the increasing focus on strengthening One Health capacity building on global level, challenges remain in devising and implementing real-world interventions particularly in the Asia-Pacific region. Recognizing these gaps, the One Health Action Commission (OHAC) was established as an academic community for One Health action with an emphasis on research agenda setting to identify actions for highest impact. MAIN TEXT: This viewpoint describes the agenda of, and motivation for, the recently formed OHAC. Recognizing the urgent need for evidence to support the formulation of necessary action plans, OHAC advocates the adoption of both bottom-up and top-down approaches to identify the current gaps in combating zoonoses, antimicrobial resistance, addressing food safety, and to enhance capacity building for context-sensitive One Health implementation. CONCLUSIONS: By promoting broader engagement and connection of multidisciplinary stakeholders, OHAC envisions a collaborative global platform for the generation of innovative One Health knowledge, distilled practical experience and actionable policy advice, guided by strong ethical principles of One Health.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.319
Teacher spread0.296 · 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.

Study designObservational
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

Citations28
Published2024
Admission routes1
Has abstractyes

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