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Record W2339254344

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

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

Bibliographic record

VenueCGSPace A Repository of Agricultural Research Outputs (Consultative Group for International Agricultural Research) · 2016
Typereport
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
FundersInternational Development Research Centre
KeywordsChinaSoutheast asiaPolitical scienceField (mathematics)GeographyEnvironmental planningPublic relationsHistoryArchaeologyAncient history
DOInot available

Abstract

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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.

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)0.016
metaresearch head score (Gemma)0.005
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 score0.091
Threshold uncertainty score0.180

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0130.007
Scholarly communication0.0070.004
Open science0.0020.013
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0070.000

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 head0.157
GPT teacher head0.393
Teacher spread0.236 · 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".

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Citations1
Published2016
Admission routes1
Has abstractno

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