Mind the data gaps: Comparing the quality of data sources for maternal health services in Cameroon
Bibliographic record
Abstract
Numerous sources of routine data exist but there is limited information on how they relate or complement each other to improve data availability and the quality of data collected. This paper compares data coverage and completeness on selected maternal health service indicators between (1) a performance-based financing(PBF) database, (2) the national health information system, and (3) health facility registers in selected districts in Cameroon. Data on antenatal care, skilled birth delivery and family planning were collected from 2010 to 2020 in three purposively selected districts (Buea, Limbe and Tiko) in the southwest region of Cameroon. The coverage and completeness of data from the performance-based financing database, the district health information system (dhis2, a national system) and health facility registers were compared. Data sources for the performance-based financing database and the district health information system are based on data generated from health facilities. Among the 90 health facilities in the three districts, 13 (14.5 %) facilities could not be accessed due to ongoing political conflict. Therefore, data were collected from 77 health facilities. Of the 77 facilities, half were public, a third private, and the remainder para-public (13 %) or confessional (5 %). Approximately seven registers at each health facility included data on maternal and child health. Problems of these data included incomplete coverage, misplacement of records, and incomplete data in the records identified. There was inconsistency across all sources. dhis2 collected antenatal care only for the first and fourth visits and PBF collected data for any antenatal care visits without specifying the visit number and health facility collected data for all antenatal care visits. The introduction of dhis2 and PBF programs has strengthened the availability of data in electronic format. Generally, we noted important gaps and heterogeneity in data reporting as well as incomplete data across health sectors and districts. There is need to transform the way data are collected at health facilities and there is also need for capacity building and better data governance to improve data quality and use. This will ensure that reliable, consistent, accurate, and actionable data are available to inform policy towards achieving Universal Health Coverage.
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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 | 0.105 | 0.384 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.031 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| 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".