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Record W2619888188 · doi:10.1186/s12889-017-4299-3

The Camino Verde intervention in Nicaragua, 2004–2012

2017· article· en· W2619888188 on OpenAlexaff
Jorge Arosteguí, Robert J. Ledogar, Joséfina Coloma, Carlos Hernández-Álvarez, Harold Suazo-Laguna, Alvaro Cárcamo, Rosa María Reyes, Alejandro Belli, Neil Andersson, Eva Harris

Bibliographic record

VenueBMC Public Health · 2017
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineBiostatisticsPublic healthIntervention (counseling)EpidemiologyEnvironmental healthCape verdeNursingEthnologyPathology

Abstract

fetched live from OpenAlex

Camino Verde (the Green Way) is an evidence-based community mobilisation tool for prevention of dengue and other mosquito-borne viral diseases. Its effectiveness was demonstrated in a cluster-randomised controlled trial conducted in 2010-2013 in Nicaragua and Mexico. The Nicaraguan arm of the trial was preceded, from 2004 to 2008, by a feasibility study that provided valuable lessons and trained facilitators for the trial itself. Here, guided by the Template for Intervention Description and Replication (TIDieR), we describe the Camino Verde intervention in Nicaragua, presenting its rationale, its time and location, activities, materials used, the main actors, modes of delivery, how it was tailored to encourage community engagement, modifications made from the feasibility study to the trial itself, and how fidelity to the process originally designed was maintained. We also present information on costs and discuss the place of this study within the literature on implementation science. TRIAL REGISTRATION: ISRCTN27581154 .

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)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.048
GPT teacher head0.367
Teacher spread0.320 · 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 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

Citations16
Published2017
Admission routes1
Has abstractyes

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