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Record W4389767159 · doi:10.47191/ijcsrr/v6-i12-32

Factors Influencing the Participation of Mothers with Toddlers Aged 2-12 Months in the Pneumococcal Conjugate Vaccine Program in Metro, Lampung, Indonesia

2023· article· en· W4389767159 on OpenAlexaff
Rika Pratiwi, Betta Kurniawan, Bayu Anggileo Pramesona

Bibliographic record

VenueInternational Journal of Current Science Research and Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsWiLAN (Canada)
Fundersnot available
KeywordsMedicinePneumococcal conjugate vaccinePediatricsPneumoniaImmunization programLogistic regressionImmunizationUnder-fiveEnvironmental healthFamily medicineStreptococcus pneumoniaeImmunologyInternal medicine

Abstract

fetched live from OpenAlex

Pneumonia (pneumonitis) is an infectious disease that often attacks children under five. One way to prevent and control pneumonia cases is through the Pneumococcal Conjugate Vaccine (PCV) immunization program targeting toddlers aged two months to 12 months. However, the achievement of Universal Child Immunization (UCI) in Metro Lampung, Indonesia, is below the target. This research aims to determine the factors that influence the participation of mothers of toddlers aged 2-12 months in the PCV immunization program. This cross-sectional study was conducted on 208 patients with mothers of toddlers aged 2-12 months in the PCV immunization program in Metro Lampung, Indonesia, from July to October 2023. Variable measurements were carried out using a questionnaire. The chi-square test and logistic regression were used for data analysis. The research results show that factors that have a significant influence on the participation of mothers of toddlers aged 2-12 months in the PCV program are knowledge (OR= 7.32; 95% CI= 3.38-18.85), family support OR=6, 71; 95% CI=3.09-14.57) and exposure to information media (OR=4.28; 95% CI=1.94-9.42). Mothers with toddlers aged 2-12 months should participate in the PCV program to prevent pneumonia in children.

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)0.009
metaresearch head score (Gemma)0.001
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 score0.066
Threshold uncertainty score0.316

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.162
GPT teacher head0.489
Teacher spread0.326 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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