Demographic and health surveys showed widening trends in polio immunisation inequalities in Guinea
Bibliographic record
Abstract
AIM: This study examined trends in absolute and relative socio-economic, gender and geographical inequalities in the coverage of polio immunisation in Guinea, West Africa, from 1999 to 2016. METHODS: Data from the 1999, 2005 and 2012 Guinea Demographic and Health Survey and the 2016 Guinea Multiple Indicator Cluster Survey were analysed using the World Health Organization's health equity assessment toolkit. We disaggregated polio immunisation coverage using five equity stratifiers: household economic status, maternal educational level, place of residence, child's gender and region. The four summary measures used were the difference, ratio, population attributable risk and population attributable fraction. A 95% confidence interval (CI) was constructed around point estimates to measure statistical significance. RESULTS: A total of 4778 1-year-old children were included. Polio immunisation coverage in 1999, 2005, 2012 and 2016 were 43.4%, 50.7%, 51.2% and 38.6%, respectively. Socio-economic and geographical inequalities in polio immunisation favoured children with educated mothers who came from richer families living in urban areas. There were also differences in the eight regions over the 1999-2016 study period. CONCLUSION: Targeting children from disadvantaged subgroups must be prioritised to ensure equitable immunisation services that help to eradicate polio in Guinea.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".