Knowledge and practice of primary healthcare workers on the routine childhood immunization schedule in Osun State
Bibliographic record
Abstract
Background: Despite global progress in immunization, Nigeria continues to face challenges in achieving adequate vaccination coverage. This study examined the knowledge and practices of primary healthcare workers regarding routine childhood immunization in Osun State, where coverage exceeds national averages but remains below global targets. Methods: A descriptive cross-sectional study was conducted among 273 primary healthcare workers across 48 primary health centers in Osun State. Results: There was a significant disparity between the knowledge (86.5%) and practice of routine childhood immunization (40.6%). Key gaps included inadequate vaccine storage (with 59% failing to maintain temperature records) and inconsistent caregiver communication (only 53.7% consistently obtained consent). Training deficiencies were evident, with 12.3% of staff lacking pre-service immunization training and 60% not having received refresher training within the previous six months. Conclusions: These findings suggest that achieving better immunization outcomes requires more than just knowledge transfer. Health system strengthening should focus on improving cold chain infrastructure, implementing regular competency-based training, and strengthening supervision mechanisms. The study emphasizes the importance of ongoing support for healthcare workers to bridge the gap between knowledge and practice in routine immunization services.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".