Three Waves of Data Use Among Health Workers: The Experience of the Better Immunization Data Initiative in Tanzania and Zambia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it