A Model for Perinatal Information Management System in Iran
Bibliographic record
Abstract
Objective (s): Mothers and infants are considered as vulnerable groups. Maintaining and improving their health are very much dependent on quality and accuracy of health information. Thus providing timely information is essential for monitoring and evaluating maternal and neonatal health condition. Since this information is organized in perinatal health information systems in developed countries, this study was conducted to present a model of perinatal information management system in Iran. Methods: This was an applied and mixed method study (qualitative-comparative and Delphi) conducted in 2013. Perinatal information management systems in Australia, Canada, New Zealand, America, England and Iran were studied and compared via library sources, the Internet, available documents, and correspondence with authors. Comparative tables were provided to analyze the data. Then, the initial model for perinatal information management system was suggested and a questionnaire was developed. The questionnaire was tested by Delphi technique in two rounds. Finally, items by more than 75 percent were added to the model. Results: The final model was presented in three major topics: goals, structures and mechanism of data collection. In this model, 32 goals were confirmed. Responsible organizations, monitoring organizations, committees and centers of data production in the axis of structure were determined. In data collection mechanism, data sources, media types, long range data transmission, data collection processes, privacy and data security procedures were also examined. Conclusion: The proposed model is expected to serve as a basis for improving the quality of perinatal health information, exchanging health data and achieving an integrated information management system.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".