Three Waves of Data Use Among Health Workers: The Experience of the Better Immunization Data Initiative in Tanzania and Zambia
Bibliographic record
Abstract
The governments of Tanzania and Zambia identified key data-related challenges affecting immunization service delivery including identifying children due for vaccines, time-consuming data entry processes, and inadequate resources. To address these challenges, since 2014, the countries have partnered with PATH's Better Immunization Data Initiative to design and deploy a suite of data quality and use interventions. Two key aspects of the interventions were an electronic immunization registry and tools and practices to strengthen a culture of data use. As both countries deployed the interventions, 3 distinct changes in data use emerged organically. This article provides a detailed summary of these 3 phases or waves, based mostly on qualitative data or observation: (1) strengthening data collection using new data collection tools and processes and increasing efficiency of health workers; (2) improving data quality regarding accuracy and completeness; and (3) increasing use of data to take action to strengthen their work and for programmatic decision making. These waves clearly demonstrated the growing ability of health workers to move from data collectors to data analyzers who began to focus on the data quality and then the value of using the data in their day-to-day activities.
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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 | 0.026 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".