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Record W2106066919 · doi:10.1186/2049-9957-3-36

Identifying the impediments and enablers of ecohealth for a case study on health and environmental sanitation in Hà Nam, Vietnam

2014· article· en· W2106066919 on OpenAlexafffund
Vi Nguyen, Hung Nguyen‐Viet, Phuc Pham-Duc, Craig Stephen, Scott A. McEwen

Bibliographic record

VenueInfectious Diseases of Poverty · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicWastewater Treatment and Reuse
Canadian institutionsUniversity of CalgaryUniversity of GuelphPublic Health Agency of Canada
FundersInternational Livestock Research InstituteInternational Development Research CentrePublic Health AgencyPublic Health Agency of Canada
KeywordsSanitationEnvironmental healthPublic healthOpen defecationGeographyMedicineEnvironmental protectionEnvironmental planningNursing

Abstract

fetched live from OpenAlex

BACKGROUND: To date, research has shown an increasing use of the term "ecohealth" in literature, but few researchers have explicitly described how it has been used. We investigated a project on health and environmental sanitation (the conceptual framework of which included the pillars of ecohealth) to identify the impediments and enablers of ecohealth and investigate how it can move from concept to practice. METHODS: A case study approach was used. The interview questions were centred on the nature of interactions and the sharing of information between stakeholders. RESULTS: The analysis identified nine impediments and 15 enablers of ecohealth. Three themes relating to impediments, in particular-integration is not clear, don't understand, and limited participation-related more directly to the challenges in applying the ecohealth pillars of transdisciplinarity and participation. The themes relating to enablers-awareness and understanding, capacity development, and interactions-facilitated usage of the research results. By extracting information on the environmental, social, economic, and health aspects of environmental sanitation, we found that the issue spanned multiple scales and sectors. CONCLUSION: The challenge of how to integrate these aspects should be considered at the design stage and throughout the research process. We recommend that ecohealth research teams include a self-investigation of their processes in order to facilitate a comparison of moving from concept to practice, which may offer insights into how to evaluate the process.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.268

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)0.0000.000

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 head0.011
GPT teacher head0.263
Teacher spread0.252 · 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 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

Citations13
Published2014
Admission routes2
Has abstractyes

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