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Record W2131713617 · doi:10.12927/whp.2011.22172

Knowledge of Malaria and Preventive Measures among Pregnant Women Attending Antenatal Clinics in a Rural Local Government Area in Southwestern Nigeria

2011· article· en· W2131713617 on OpenAlexvenueno aff
Stella Akinleye, IkeOluwapo O. Ajayi

Bibliographic record

VenueWorld health & population · 2011
Typearticle
Languageen
FieldMedicine
TopicMalaria Research and Control
Canadian institutionsnot available
FundersFogarty International Center
KeywordsMalariaMedicineLocal government areaEnvironmental healthHealth educationPublic healthFamily medicinePregnancyGovernment (linguistics)Rural areaLocal governmentNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: This study determined the level of knowledge of malaria and preventive measures among pregnant women and its influence on the uptake of preventive measures. METHODS: A cross-sectional survey was carried out among 209 participants selected from pregnant women attending antenatal clinics in primary healthcare centres in Irepodun/Ifelodun, a local government area in Ekiti state, Nigeria. RESULTS: Knowledge of malaria was found to be very good, average and poor among two (1.0%), 165 (78.9%) and 42 (20.1%) respondents, respectively. Of the 109 (52.2%) respondents who had heard about intermittent preventive treatment, eight (7.3%) scored "very good" on knowledge, while 53 (48.6%) and 48 (44.1%) scored "average" and "poor," respectively. Of the 144 (68.9%) respondents who had heard about insecticide-treated nets, 95 (66.0%) scored "good" on knowledge, while 49 (34.0%) scored "poor." Factors that significantly influenced knowledge about malaria were occupation, level of education, months at first appearance at antenatal clinic and transportation cost. Knowledge significantly influenced uptake of insecticide-treated nets and intermittent preventive treatment in pregnancy ( p < .05). CONCLUSION: There is a need to intensify efforts to provide health education on malaria and preventive measures as well as to encourage preventive practices among pregnant women.

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)0.001
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 score0.030
Threshold uncertainty score0.987

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.035
GPT teacher head0.318
Teacher spread0.283 · 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

Citations13
Published2011
Admission routes1
Has abstractyes

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