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Record W2107230188 · doi:10.1177/1010539513475648

Ethnic Disparities in Routine Immunization Coverage

2013· article· en· W2107230188 on OpenAlexaboutno aff
Nida Tariq Siddiqui, Aatekah Owais, Ajmal Agha, Mehtab S. Karim, Anita K. M. Zaidi

Bibliographic record

VenueAsia Pacific Journal of Public Health · 2013
Typearticle
Languageen
FieldMedicine
TopicViral Infections and Immunology Research
Canadian institutionsnot available
FundersFogarty International Center
KeywordsMedicineVaccinationSocioeconomic statusLogistic regressionDemographyImmunizationDemographicsPopulationPoliomyelitisEthnic groupQuarter (Canadian coin)Environmental healthPediatricsGeographyImmunologyAntibody

Abstract

fetched live from OpenAlex

Karachi is the only mega city in the world with persistent poliovirus transmission. We determined routine childhood immunization rates in Karachi and identified predictors of vaccine completion. A population-based cross-sectional survey was conducted in Karachi between August and September 2008. Data on demographics, socioeconomic, and DTP3 vaccination status in children 12 to 23 months old were collected. Logistic regression was used to identify predictors of vaccination completion. Overall, 1401 participants were approached; 1391 consented to participate. Of these, 1038 (75%) were completely vaccinated. Punjabi families had the highest DTP3 coverage (82%), followed by Urdu-speaking families (79%). Pashtun (67%) and Bengali (48%) families had the lowest vaccine coverage. Children of mothers with ≥ 12 years of schooling (OR = 25.4; 95% CI = 5.7-113.1) were most likely to be vaccinated. A quarter of study participants were unvaccinated. Targeted strategies for boosting DTP3 rates in communities with low immunization coverage are essential for polio eradication in Karachi.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.019
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.080
GPT teacher head0.365
Teacher spread0.285 · 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

Citations24
Published2013
Admission routes1
Has abstractyes

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