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Extending anthelminthic coverage to non‐enrolled school‐age children using a simple and low‐cost method

2001· article· en· W2046318344 on OpenAlexaff
Antonio Montresor, Mahdi Ramsan, Hababu M. Chwaya, Haji Ameir, Ali Foum, Marco Albonico, Theresa W. Gyorkos, Lorenzo Savioli

Bibliographic record

VenueTropical Medicine & International Health · 2001
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsMcGill University
Fundersnot available
KeywordsDewormingMedicineDeveloping countryLimitingPediatricsTest (biology)Family medicineAge appropriateDemographyEnvironmental healthPsychologyHelminthsImmunology

Abstract

fetched live from OpenAlex

School health programmes are the basis of the strategy defined by WHO to reduce morbidity due to soil-transmitted nematodes and schistosomes in school age populations in developing countries. However, low rates of school enrollment can be a major factor limiting their success. In the present study enrolled children were informed by teachers on the date of the next deworming campaign and were invited to pass along this information to parents, siblings and friends of school-age. On the day of the deworming campaign, teachers were instructed to administer anthelminthics to enrolled and not enrolled school-age children present at school. In the month following the treatment day, information about coverage was collected by questionnaire in 257 households in two regions of Unguja Island, Zanzibar. Over 89% of school age children resulted treated (98.9% of those enrolled plus 60% of those not enrolled). The additional cost of treating non-enrolled is limited to the cost of providing additional doses of anthelminthic drug in each school. Non-enrolled school age children can easily, successfully and inexpensively included in school based deworming campaign. Managers of control programmes are invited to test this method adapting it in their particular and cultural environment.

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 score0.193
Threshold uncertainty score0.788

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)0.0010.000

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 head0.034
GPT teacher head0.407
Teacher spread0.373 · 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

Citations46
Published2001
Admission routes1
Has abstractyes

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