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Record W1825498260 · doi:10.12927/whp.2015.24267

Geographical/Ecological Differentials in Insecticide-Treated Net Use among Under-Five Children in Somolu Local Government Area, Lagos State

2014· article· en· W1825498260 on OpenAlexvenueno aff
O. O. Ojo, IkeOluwapo O. Ajayi, Taiwo Samson Awolola

Bibliographic record

VenueWorld health & population · 2014
Typearticle
Languageen
FieldMedicine
TopicMosquito-borne diseases and control
Canadian institutionsnot available
Fundersnot available
KeywordsMalariaLocal government areaMosquito netEnvironmental healthSocioeconomicsGeographyBed netsMosquito controlTransmission (telecommunications)AnophelesPublic healthInterviewVector (molecular biology)Environmental protectionLocal governmentMedicinePopulationBiology

Abstract

fetched live from OpenAlex

Malaria control efforts currently lay emphasis on reducing transmission by limiting human-vector contact. More studies have been carried out on mosquito avoidance practices in the rural areas, leaving the urban areas understudied. This study was conducted to identify knowledge of malaria transmission and to investigate geographical/ecological differentials in the use of insecticide-treated nets (ITNs) among caregivers of under-fives in Somolu Local Government Area, Lagos State. A household survey was conducted by interviewing 394 female caregivers of under-fives selected using the WHO Lot Quality Technique from communities stratified based on level of planning and drainage. The mean age of the respondents was 33.6 ± 7.7 years. Malaria transmission was attributed mostly to mosquito bites in all strata: S1 (58.3%), S2 (56.1%) and S3 (61.4%). Mosquito net was mentioned as a preventive measure by: 59.3% (S1), 80.7% (S2) and 64.3% (S3). Ownership of long-lasting insecticidal nets was: 76.0% (S1), 75.4% (S2) and 68.6% (S3), and of these, 73.1% (S1), 70.7% (S2) and 72.4% (S3) reported that their child slept under the net the night before the survey. There is a need to reinforce education on transmission and ownership of ITNs especially among caregivers in unplanned, poorly drained communities.

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 score0.056
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.014
GPT teacher head0.273
Teacher spread0.259 · 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

Citations2
Published2014
Admission routes1
Has abstractyes

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