Developing a Framework for Electronic Health Record of Students: A Case Study of West Azerbaijan Province Schools
Bibliographic record
Abstract
Background: In order to obtain information on the health status of students and a healthy community, having access to health information is critical. Most health information systems designed for adults have data recording limitations for children. The presents study develops the Electronic Health Record of students.Method: The present study is a descriptive - cross sectional study performed in 2015 in primary schools in West Azerbaijan Province. The research tools included the questionnaire and checklist completed 25 school health educators and 5 school health volunteers of health networks. The data elements were presented to the school health educators and three pediatricians. The content validity was determined and its reliability index was measured by Cronbach's alpha test with SPSS version 22.Results: Students’ Personal Health Record is a booklet called “Health Certificate” that includes 100% of the demographic information, screening data and students’ immunization but it lacked sequence and integrity required to obtain the health information. The school care data and the results of initial admission were documented in none of the schools. Due to low security and privacy of paper information, the reports were unreliable. According to the minor role of the school health educators in the process of documentation of health information, data recording process was reviewed. The revised data elements were approved by 100% without any opposite opinion.Conclusion: Low security and confidentiality of students’ health information indicates the need to create electronic systems. Designing electronic health records can be a major step in creating a database with the ability to communicate and exchange electronic health records.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 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".