Gender inequity and age-appropriate immunization coverage in India from 1992 to 2006
Bibliographic record
Abstract
BACKGROUND: A variety of studies have considered the affects of India's son preference on gender differences in child mortality, sex ratio at birth, and access to health services. Less research has focused on the affects of son preference on gender inequities in immunization coverage and how this may have varied with time, and across regions and with sibling compositions. We present a systematic examination of trends in immunization coverage in India, with a focus on inequities in coverage by gender, birth order, year of birth, and state. METHODS: We analyzed data from three consecutive rounds of the Indian National Family Health Survey undertaken between 1992 and 2006. All children below five years of age with complete immunization histories were included in the analysis. Age-appropriate immunization coverage was determined for the following antigens: bacille Calmette-Guérin (BCG), oral polio (OPV), diphtheria, pertussis (whooping cough) and tetanus (DPT), and measles. RESULTS: Immunization coverage in India has increased since the early 1990s, but complete, age-appropriate coverage is still under 50% nationally. Girls were found to have significantly lower immunization coverage (p<0.001) than boys for BCG, DPT, and measles across all three surveys. By contrast, improved coverage of OPV suggests a narrowing of the gender differences in recent years. Girls with a surviving older sister were less likely to be immunized compared to boys, and a large proportion of all children were found to be immunized considerably later than recommended. CONCLUSIONS: Gender inequities in immunization coverage are prevalent in India. The low immunization coverage, the late immunization trends and the gender differences in coverage identified in our study suggest that risks of child mortality, especially for girls at higher birth orders, need to be addressed both socially and programmatically. ABSTRACT IN HINDI : See the full article online for a translation of this abstract in Hindi.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".