Magnitudes of Immunization Dropout Rate and Predictors for 12-23 Months Aged Children in Abobo District Southwest Ethiopia
Bibliographic record
Abstract
Vaccination is the epicenter of preventive care for good children's health outcomes in each nation. Nevertheless, a number of factors have been hindering the attainment of targets from providing complete vaccination in different nations. This study aims to assess predictors of immunizations in 12-23 months aged children in Abobo District, Gambela regions southwest Ethiopia. Method: A community-based cross-sectional study was employed in 436 pairs of mothers to children aged 12–23 months from 12 marches---27 April 2019. The study participant was recruited by multistage-sampling were used for each kebele. Data were entered into Epi-Data version 3.1 after cleaning and coded, exported to STATA/SE-14/R logistic regression analysis. Variables with P-value <0.25 in bivariate logistic regression were transported into multivariable logistic regression. A variable with 95%CI in AOR was used as claim predictors for the dropout rate. Results: The overall dropout rate of immunization from completion was found 25.8% (95%CI: 21.5--30.2). Factors like mothers did not attend ANC (AOR= 4.59, 95% CI: 2.58, 7.84), being home delivery (AOR=6.46, 95% CI: (3.5--- 11.4), postponed last immunization scheduled (AOR=3.44, 95% CI: 1.98---5.97), children ill during measles vaccine (AOR=1.83, 95% CI: (1.02---3.28), Mothers refused ≥30 minutes for vaccine service waiting (AOR=3.58, 95% CI: (1.99, 6.44) were significantly associated with immunization dropout out. Conclusion: The immunization dropout rate was unacceptable and higher compared to WHO reference (<10%). Home delivery postponed measles vaccine, child illness, ANC status Service refusal ≥30 minutes waiting for the vaccine were independently associated with dropout.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".