MétaCan
Menu
Back to cohort
Record W2029431112 · doi:10.4061/2011/765125

Malaria Knowledge, Concern, Land Management, and Protection Practices among Land Owners and/or Managers in Lowland versus Highland Ecuador

2011· article· en· W2029431112 on OpenAlexafffund
Lauren Pinault, Fiona F. Hunter

Bibliographic record

VenueMalaria Research and Treatment · 2011
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsBrock University
FundersPontificia Universidad Católica del EcuadorUniversidad San Francisco de QuitoNatural Sciences and Engineering Research Council of CanadaInternational Development Research Centre
KeywordsMalariaGeographyHabitatMalaria preventionAnophelesEnvironmental healthSocioeconomicsMosquito controlEnvironmental resource managementBusinessEnvironmental planningEnvironmental protectionEcologyMedicineBiologyPopulationHealth servicesEconomicsImmunology

Abstract

fetched live from OpenAlex

To control malaria effectively, it is essential to understand the current knowledge, beliefs, concerns, land management practices, and mosquito bite protection methods in use by citizens. This study presents a comparative, quantitative, interview-based study of land owners and/or managers (n = 262) in the Ecuadorian lowlands (presently considered malarious) (n = 131) and highlands (potentially malarious in the future) (n = 131). Although respondents had a strong understanding of where the disease occurs in their own country and of the basic relationship among standing water, mosquitoes, and malaria, about half of respondents in potential risk areas denied the current possibility of malaria infection on their own property. As well, about half of respondents with potential anopheline larval habitat did not report its presence, likely due to a highly specific definition of suitable mosquito habitat. Most respondents who are considered at risk of malaria currently use at least one type of mosquito bite prevention, most commonly bed nets.

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 score0.055
Threshold uncertainty score0.665

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 head0.147
GPT teacher head0.375
Teacher spread0.228 · 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

Citations5
Published2011
Admission routes2
Has abstractyes

Explore more

Same venueMalaria Research and TreatmentSame topicMosquito-borne diseases and controlFrench-language works237,207