Analysis on effects of quality control system of prevention of mother-to-child transmission of AIDS information on quality of data
Bibliographic record
Abstract
Objective:To explore the feasibility of establishing the quality control system of prevention of mother-to-child transmission of AIDS information,provide a scientific basis for improving and promoting the program.Methods:According to the prevalence of AIDS,Kaiyuan city and Longyang district in Yunnan province were selected as polity sites.The main methods include the establishment of quality control group,selections of maternal and child health hospital,CDC and a certain number of midwifery agencies,every quarter in 2008,a data quality control was made,and the changes of quality of data were analysed.Results:Through quality control,the hospitals establishing the original accounts increased from 60.00% to 100.00%,and the midwifery institutions which information staff fixed HIV-testing misreporting rate of pregnant women decreased from 1.58% to 0.15%,HIV-testing misreporting rate of marriage registration decreased from 0.63% to 0,HIV-positive misreporting rate of pregnant women decreased from 4.82% to 0.HIV-testing misreporting rate of pregnant women in general hospital was higher than that in maternal and child health hospital. The information staff turnover affected the quality of data.Conclusion:It's very important and necessary that the establishment of quality control system of prevention of mother-to-child transmission of AIDS information for standardizing information management and improving data quality.
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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 | 0.038 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".