Additional file 1 of Can routine health facility data be used to monitor subnational coverage of maternal, newborn and child health services in Uganda?
Bibliographic record
Abstract
Additional file 1: Table S1. Summary of results of regression of reported annual numbers of ANC1 and penta1 by national, subregional and district levels. Table S2. Absolute difference between two health facility data coverage estimates with either population projection denominator or health facility data derived denominator and the UDHS 2016 results (or UMIS 2018 in case of IPT2). Figure S1. Completeness of reporting for MCH by district, 2015–2019, DHIS2, Uganda. Figure S2. Population coverage of measles vaccination among infants, intermittent preventive therapy second dose (IPT 2) among pregnant women and deliveries in health facilities and antenatal care 4th visit by subregion according to Uganda DHS 2016 (bar), and derived from health facility reported data according to denominator method (population projection – dash and health facility data derived - dots). Figure S3. Percent of districts with less plausible coverage estimates according to denominator method (population projection and health facility data derived).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.796 | 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".