Additional file 1 of The burden of recording and reporting health data in primary health care facilities in five low- and lower-middle income countries
Bibliographic record
Abstract
Additional file 1: Appendix Table 1a. Number of facilities providing specific services in five countries (2016–2017). Appendix Table 1b. Facility attributes (Median staffing) in five countries (2016–2017). Appendix Table 2. The Desk Review national inventory of registers mandated and verified in use, 80 PHC facilities, five countries (2016–2017). Appendix Table 3a. High use registers (OPD, ANC, FP, EPI) – estimated consultation and recording time in five countries (2016–2017). Appendix Table 3b. Disease-specific registers– estimated consultation and recording time in five countries (2016–2017). Appendix Table 4a the number of consultations observed by service area in five countries (2016–2017). Appendix Table 4b Comparing the mean consultation and register completion time (observed and self-reported) in five countries (2016–2017). Appendix Table 5 – The Desk Review national inventory of reporting forms mandated and verified in use, 80 PHC facilities, five countries (2016–2017). Appendix Table 6 The number of forms confirmed in use and the estimated reporting time (median) in monthly by service groupings in five countries (2016–2017). Appendix Table 7 – Distribution of reporting forms (cells and estimated time), by service area, in five countries (2016–2017).
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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 | 0.003 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.011 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.658 | 0.074 |
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".