Routine immunization community surveys as a tool for guiding program implementation in Kaduna state, Nigeria 2015–2016
Bibliographic record
Abstract
BACKGROUND: Routine childhood immunization remains an important strategy for achieving polio eradication and maintaining a polio-free world. To address gaps in reported administrative coverage data, community surveys were conducted to verify coverage, and guide strategic interventions for improved coverage. METHODS: We reviewed the conduct of community surveys by World Health Organization (WHO) field volunteers deployed as part of the surge capacity to Kaduna state and the use of survey results between July 2015 and June 2016. Monthly and quarterly collation and use of these data to guide the deployment of various interventions aimed at strengthening routine immunization in the state. RESULTS: Over 97,000 children aged 0-11 months were surveyed by 138 field volunteers across 237 of the 255 wards in Kaduna state. Fully or appropriately immunized children increased from 67% in the fourth quarter of 2015 to 76% by the end of the second quarter of 2016. Within the period reviewed, the number of local government areas with < 80% coverage reduced from eight to zero. CONCLUSIONS: The routine conduct of community surveys by volunteers to inform interventions has shown an improvement in the vaccination status of children 0-11 months in Kaduna state and remains a useful tool in addressing administrative data quality issues.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".