Data for decision making: using a dashboard to strengthen routine immunisation in Nigeria
Bibliographic record
Abstract
Availability of reliable data has for a long time been a challenge for health programmes in Nigeria. Routine immunisation (RI) data have always been characterised by conflicting coverage figures for the same vaccine across different routine data reporting platforms. Following the adoption of District Health Information System version 2 (DHIS2) as a national electronic data management platform, the DHIS2 RI Dashboard Project was initiated to address the absence of some RI-specific indicators on DHIS2. The project was also intended to improve visibility and monitoring of RI indicators as well as strengthen the broader national health management information system by promoting the use of routine data for decision making at all governance levels. This paper documents the process, challenges and lessons learnt in implementing the project in Nigeria. A multistakeholder technical working group developed an implementation framework with clear preimplementation; implementation and postimplementation activities. Beginning with a pilot in Kano state in 2014, the project has been scaled up countrywide. Nearly 34 000 health workers at all administrative levels were trained on RI data tools and DHIS2 use. The project contributed to the improvement in completeness of reports on DHIS2 from 53 % in first quarter 2014 to 81 % in second quarter 2017. The project faced challenges relating to primary healthcare governance structures at the subnational level, infrastructure and human resource capacity. Our experience highlights the need for early and sustained advocacy to stakeholders in a decentralised health system to promote ownership and sustainability of a centrally coordinated systems strengthening initiative.
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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 | 0.051 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".