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Enregistrement W2339254344

Advancing ecohealth in Southeast Asia and China: Lessons from the Field Building Leadership Initiative

2016· report· en· W2339254344 sur OpenAlexfundno aff
Steven Lâm, Wiku Adisasmito, Phuc Pham Ðuc, Pattamaporn Kittayapong, Hung Nguyen‐Viet

Notice bibliographique

RevueCGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) · 2016
Typereport
Langueen
DomaineArts and Humanities
ThématiqueHermeneutics and Narrative Identity
Établissements canadiensnon disponible
Organismes subventionnairesInternational Development Research Centre
Mots-clésChinaSoutheast asiaPolitical scienceField (mathematics)GeographyEnvironmental planningPublic relationsHistoryArchaeologyAncient history
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

FBLI country sites Executive summaryIntensification of crop and livestock production can improve food, nutrition, and income security; however, intensification can also lead to increased health risks, environmental degradation, and biodiversity loss.This is especially true in Southeast Asia and China, regions facing rapid economic growth.To address this complex challenge, a better understanding of the interactions between agricultural practices, human health, and ecosystems is required.The Field Building Leadership Initiative (FBLI), supported by the International Development Research Centre (IDRC), has been working to understand and address intensive agricultural practices and associated health risks in Southeast Asia and China.Developed jointly by research centres in China, Indonesia, Thailand and Vietnam, and launched in 2012, this five-year initiative allows researchers and their partners to carry out research, capacity building, and knowledge translation to inform practice and policy. Key messages Intensive agricultural practices can have far-reaching impacts on health and environment  Smallholder farmers play an important role in meeting the global demand for food  The livelihoods of smallholder farmers are affected both positively and negatively by agricultural intensification  Measures which are likely to help to address challenges include:-Creation and dissemination of guidelines for best agricultural practices and monitoring and evaluation of guidelines; -Long-term commitment of partnership initiatives; and -Increased investment in research and policy surrounding agriculture and health Research for developmentThe FBLI team, working with stakeholders from the onset of research for over four years, has achieved progress in improving the health of smallholder farmers.Specifically, the project created new evidence on health risks of agricultural intensification and developed innovative interventions to mitigate health risks and promote sustainable agricultural practices.The integration of FBLI research results into agricultural practices is testimony of the rigorous research efforts and productive engagement of FBLI with relevant stakeholders.Through the initiative, researchers and partners undertook research on a number of issues:  Pesticide use and its impact on human health and agricultural ecosystems in China;  Human and animal waste management in Vietnam;  Rubber plantations and vector-borne diseases in Thailand; and  Small-scale dairying in Indonesia.Ecohealth are approaches that recognize that human health and well-being are the result of a complex set of interactions between people, social and economic conditions, culture, and the natural environment.In short, human health is dependent on the health of our ecosystems.A number of achievements were noted so far: Better understanding of health risks of agricultural intensification;  Innovative products and interventions to address such health risks;  Preliminary changes observed in behaviours and practices of farmers towards more sustainable agricultural development;  Increased Ecohealth capacity of senior researchers and new generation of researchers  Increased awareness of Ecohealth among researchers and academic institutions; and  Involvement of academic institutions, NGOs, ministries, and community members in research activities through networking and engagement. Building capacity and knowledge to actionThe FBLI has been supporting the development of sustainable cohorts of Ecohealth practitioners and researchers.For example, through the FBLI's Global Health True Leader Series, a regional leadership training program, many young professionals from various fields (e.g., agriculture, health, and environment) developed their leadership skills and Ecohealth competencies.This program has reached over 400 participants from ten Asian countries.Ecohealth curricula has also been integrated in four universities in Southeast Asia and China.The FBLI supported policy advocacy, for example, policy alliance groups were formed in each project country to facilitate research knowledge sharing and uptake.These groups consisted of mid-level policy makers, senior FBLI researchers and representatives from other regional networks.FBLI is connected with Ecohealth and One Health networks in the region to promote Ecohealth approaches, including Southeast Asia One Health Network (SEAOHUN), Ecohealth Emerging Infectious Diseases Research Initiative (Eco EID), Economic Development, and Ecosystem Changes, and Emerging Infectious Diseases Risks Evaluation (ECOMORE).The team is working towards raising public awareness on agricultural intensification issues through bulletins, publications, and a growing social media presence. Moving forward and lessons learnedAs FBLI progresses into its final year, the initiative will focus its programming on data analysis and reporting, monitoring outcomes, and knowledge sharing.The next synthesis booklet is expected to be published at the end of 2016.Lessons learned:  Despite interest of researchers in using the Ecohealth approach, it is a complex undertaking requiring substantial time and skills.However, the capacity of team members in using the Ecohealth approach increased through experiences. Linking researchers to policy makers and influencing policy decisions have proven to be challenging, but processes such as word-of-mouth can help facilitate the networking.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,016
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,091
Score d'incertitude au seuil0,180

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0160,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0130,007
Communication savante0,0070,004
Science ouverte0,0020,013
Intégrité de la recherche0,0030,005
Charge utile insuffisante (le modèle a refusé de juger)0,0070,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,157
Tête enseignante GPT0,393
Écart entre enseignants0,236 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2016
Routes d'admission1
Résumé présentnon

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Même revueCGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research)Même sujetHermeneutics and Narrative IdentityTravaux en français237 207