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Record W2765452988 · doi:10.1111/zph.12413

Lifestyle, habitat and farmers' risk of exposure to tick bites in an endemic area of tick‐borne diseases in Hungary

2017· article· en· W2765452988 on OpenAlexaff
Sen Li, Linda Juhász-Horváth, Attila J. Trájer, László Pintér, Mark Rounsevell, Paula A. Harrison

Bibliographic record

VenueZoonoses and Public Health · 2017
Typearticle
Languageen
FieldImmunology and Microbiology
TopicVector-borne infectious diseases
Canadian institutionsInternational Institute for Sustainable Development
FundersSeventh Framework ProgrammeNatural Environment Research CouncilSight Research UK
KeywordsTypologyTickHabitatAgricultureGeographyEnvironmental healthSocioeconomicsEcologyVeterinary medicineBiologyMedicine

Abstract

fetched live from OpenAlex

Controlling tick bites on farmers is important to the management of tick-borne diseases and occupational health risks in agriculture. Based on an extensive household survey conducted between June and August 2015 with 219 farmers from western Hungary where tick-borne diseases are endemic, we analysed the pattern of farmers' self-reported contacts with ticks and investigated the potential interactions between farmers, landscape and the risk of exposure to tick bites. We developed a lifestyle typology based on farmers' socioeconomic profiles, farming objectives and time use patterns, and a habitat typology describing different configurations of tick habitats and agricultural areas in place of farming. We found no relationship between tick exposure risk and self-prevention. The lifestyle typology could be used to classify the risk of tick bites and the adoption of prevention measures into different levels, the difference between which could further be modified by the habitat typology. Our results suggest that (i) farmers who are frequently engaged in outdoor recreations and (ii) part-time and inexperienced farmers who have lower rate of preventive actions are likely to experience greater exposure to tick bites either in less cultivated, semi-natural habitats or in agricultural landscape with highly diverse land uses. Future disease prevention practices should take into consideration the interaction of lifestyle and habitat and the need to associate different farmer groups with different landscape configurations.

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)0.001
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 score0.017
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.021
GPT teacher head0.280
Teacher spread0.260 · 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

Citations21
Published2017
Admission routes1
Has abstractyes

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