Exploring data trends and providers' insights on measles immunization uptake in south-west Nigeria
Bibliographic record
Abstract
Introduction: measles outbreak remains a recurring episode and continues to be responsible for millions of deaths globally every year. This study examines measles immunization coverage and uncovers barriers and enablers to effective provision and uptake of measles immunization services from the supply end and provider´s perspective in a developing nation´s context. Methods: the study employed a mixed-method approach to explore trends and patterns of measles immunization uptake in Ekiti State-a state in the southwestern region of Nigeria-utilizing DHIS 2014 - 2019 data of 789,518 under 1-year children and complemented the quantitative study with key informant interviews from appointed Immunization Officers in the state. Using deductive methods, we thematically analyzed the interview data using NVivo version 12 while STATA 16 was used to analyze the quantitative data. Results: the annualized measles immunization coverage ranged between 49% and 86% from 2014 to 2019, which is below the WHO set threshold for measles infection prevention. Caregiver, geographical, human, and infrastructural factors were elicited as barriers, while potential enablers include increased public engagement and enhanced media involvement. Conclusion: while programmatic efforts are being improved nationally to drive up the uptake, this study provides baseline information for benchmarking the subsequent level of efforts and recommends improved collaboration across contextually similar states to promote program efficiency. The results can inform policy and program development, execution and direct future research on measles immunization to address uptake challenges at both local and central administration levels, especially in the aspect of surveillance and monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".