Prevention of Mother-to-Child Transmission of HIV data completeness and accuracy assessment in health facilities of the Nkangala District
Bibliographic record
Abstract
Background: Even though significant progress has been made in the roll-out and quality of the prevention of mother-to-child transmission of HIV (PMTCT) services in South Africa, the quality of patient data recording remains a challenge. Objectives: To assess PMTCT data completeness and accuracy at primary healthcare level to district level in order to assist with the improvement of the PMTCT data recording.Methods: This is a retrospective record review study which involved collecting PMTCT data on indicators which was for the period of August 2009 to January 2010. We conducted baseline facility assessments which included 72 PMTCT sites in one health district, Nkangala. We assessed the data completeness and accuracy of the data values recorded on the seven PMTCT data elements.Results: Data were only complete for less than a quarter of the time for most of the antenatal indicators (0.5% – 44%) and for the maternity indicators, data were only complete 11% of the time. Data inaccuracy was a result of recording of data values in the District Health Information System (DHIS) which were not within 10% of the data values recorded in the case registers. The results show that data were missing from the case registers, monthly summary sheets and DHIS between 30% and 99% of the time and that data elements had values recorded in the DHIS which were > 10%.Conclusion: There is a need for ongoing training on data recording procedures at all levels. To maintain data quality, healthcare data must be appropriate, organised, timely, available, accurate and complete.
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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 | 0.019 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 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".