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Record W2936773047 · doi:10.1186/s12889-019-6634-3

Community, system and policy level drivers of bovine tuberculosis in smallholder periurban dairy farms in India: a qualitative enquiry

2019· article· en· W2936773047 on OpenAlexfundno aff
Abhimanyu Singh Chauhan, Mathew Sunil George, Johanna F. Lindahl, Delia Grace, Manish Kakkar

Bibliographic record

VenueBMC Public Health · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAnimal Disease Management and Epidemiology
Canadian institutionsnot available
FundersGuru Angad Dev Veterinary and Animal Sciences UniversityInternational Development Research Centre
KeywordsEnvironmental healthPublic healthMedicinePsychological interventionThematic analysisUrbanizationQualitative researchEconomic growthBusinessSocioeconomicsNursingSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Rapid urbanization has led to expansion of peri-urban fringes, where intensive, industry-style livestock rearing has led to emerging vulnerabilities at the human-animal-environment interface. This study was undertaken to understand the health system and farm-level factors that influenced the risk of transmission of bovine Tuberculosis (bTB) in animals and humans in peri-urban smallholder dairy farms of India. METHODS: Thematic guides were developing through literature review and expert consultation. In-depth interviews were conducted till attainment of saturation. Identification of core themes was followed by etiological enquiry and generation of a conceptual model. RESULTS: Veterinarians were consulted as a last resort after home-remedies and quacks had failed. Damage control measures, especially with respect to- selling or abandoning sick animals, added to the risk of disease transmission. Although civic authorities believed in the adequacy of a functioning laboratory network, end users were aggrieved at the lack of services. Despite the presence of extension services, knowledge and awareness was limited, promoting risky behaviour. The absence of cogent policies in dealing with bTB was a significant barrier. Stakeholders did not consider bTB to be a major concern. It is possible that they underestimate the problem. CONCLUSION: The current study helps to identify gaps which need to be addressed through collaborative research, and OneHealth interventions to build community awareness.

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)0.003
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 score0.053
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.143
GPT teacher head0.347
Teacher spread0.204 · 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

Citations18
Published2019
Admission routes1
Has abstractyes

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