Minimum Data Sets of Perinatal Period for Iran: A Delphi Study
Bibliographic record
Abstract
Introduction: A Minimum Data Set (MDS) is a core set of data elements agreed by the National Health Information Management Group in Australia for mandatory collection and reporting of data at a national level. It has important role in the health care industry for data exchange and implementation of electronic health records. The aim of this study was to design of perinatal MDS for Iran. Methods: This was an applied and mixed method study (qualitative - comparative and Delphi) conducted in 2013. For the first step, perinatal MDS in Australia, Canada, New Zealand, America, England and Iran were studied and compared via library sources, the Internet, correspondance with foreign authors, and available forms. The instrument of the study were data collection forms and a questionnaire that content validity of which was determined by the experts of the field. Then, the initial model for MDS of perinatal period was suggested. The questionnaire was tested by Delphi technique in two rounds. Data analyses was performed by comparing tables and determining similarities and differences in the selective countries at the stage of comparison of MDS of perinatal period. At the stage of data validation for the model, this was accomplished with descriptive statistics (frequency, percentage, mean) and Excel software. Results: In the initial model for MDS of perinatal period, from 251 data elements and 11 sections were subjected to discussion. Finaly, 105 data elements with consensus and 122 with collective agreement were confirmed. Data elements were proposed in 15 subsets. Conclusion: Present problems has faced in country such as poor documentation, lack of data elements of standard. MDS improve the quality of perinatal care and access to accurate and timely data. Keywords: Health Information Systems; Data Set; Perinatal Care; Electronic Health Records
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.053 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".