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Record W7126678566

Améliorer la préparation et la réponse aux crises sanitaires en santé animale : étude de cas du secteur avicole en Indonésie

2025· dissertation· en· W7126678566 on OpenAlexfundno aff
Lorraine Chapot

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2025
Typedissertation
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsnot available
FundersAssociation Nationale de la Recherche et de la TechnologieGlobal Affairs CanadaUnited States Agency for International Development
KeywordsWork (physics)Public healthVulnerability (computing)Private sectorOne HealthUrbanizationInfluenza A virus subtype H5N1Animal healthGovernment (linguistics)
DOInot available

Abstract

fetched live from OpenAlex

The acceleration of globalization, environmental changes, and urbanization in recent decades have created major challenges for animal health and welfare. Events such as the 2009 influenza A (H1N1) and COVID-19 pandemics have highlighted the vulnerability of global food systems to increasingly complex multisectoral health crises with unpredictable consequences. In order to strengthen their response capabilities, many countries and organizations have adopted all-hazards incident management systems (IMS) to support a standardized and systematic response to all types of incidents.However, animal health and welfare management remains poorly integrated into these systems. Veterinary interventions are often organized on an ad hoc basis and disconnected from the overall response, leading to suboptimal management of resource and information. Furthermore, IMS models have been developed mainly by Western countries, and their applicability to other socio-economic contexts remains largely unexplored.This case study-based work focuses on the following research question: What are the practices and needs of stakeholders in the Indonesian poultry sector in terms of animal health management, and to what extent can current incident management systems address these needs?The first part of this work focuses on the study of the poultry sector in Indonesia to illustrate animal health challenges and to describe the impact and potential barriers to the implementation of management policies in a specific context. 41 semi-structured interviews were conducted with 56 public and private stakeholders in the poultry industry in five provinces of Indonesia in July 2022 to explore their priorities, needs, and capacities in terms of animal health management. In the second part of this work, 33 interviews with 45 actors from international organizations were conducted from May to July 2024 to describe the common principles of international IMS and evaluate their practical implementation. Finally, the applicability of these general frameworks to specific local context (such as the poultry sector in Indonesia) is critically discussed and pathways for improvement are suggested.The results revealed that coordination in international responses suffers from a lack of interoperability between information and communication systems, as well as the persistence of disciplinary silos. At the local level, the Indonesian case study showed that official animal health surveillance and management systems had a limited impact and revealed the predominant role of private service providers (veterinarians, pharmaceutical companies, etc.) in disseminating information during crises. The strong compartmentalization between the public and private sectors, the lack of interoperability of production and health data, and the dominance of informal communication channels contribute to the siloed management of health crises. The conventional international response, centered on the detection and eradication of outbreaks, appears ill-suited in a context of endemicity and limited resources, where infectious animal diseases are not perceived as a priority compared to other political, economic, and environmental issues.While IMS help to meet the needs for coordination and information management during crises, their operationalization both at the international and local levels still faces obstacles. There is a need to re-thing the scale of application of these systems, involve all stakeholders in the decision-making process, and strengthen organizational memory to ensure that lessons learned translate into meaningful changes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.194

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.258
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations0
Published2025
Admission routes1
Has abstractyes

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