An Investigation on the Status of Implementation of Communications and Information Management System (MCI) in Khorasan Razavi Hospitals
Bibliographic record
Abstract
BACKGROUND & OBJECTIVES: The aim of this investigation is to determine the mean scores of the possibility of implementing the MCI standards in Khorasan Razavi hospitals, from the perspective of Managers, in order to provide a suitable model for evaluating and promoting the system. METHODS: This was a Research and method (R&D) and Survey Research method, which is of the type of Cross- Sectional, descriptive-analytic Studies conducted in two steps in hospitals of Khorasan Razavi from July to December 2014. This study was approved by the Ethical Committee of Tehran University of Medical Sciences (TUMS) in 2013/6/10. About the nature and purpose of the study was explained to the participants. Were used to apply functional assessment, based on Accreditation Model. In order to collect data, two questionnaires were used, all of which were taken from the standards of MCI. The reliability and validity of the questionnaires were approved by experts.Cronbach's alphas for the questionnaires were obtained to be (0.95, 0.86), respectively. In order to analyze information, statistical analyses, including one way ANOVA, and Independent sample t-test were used. RESULTS: The mean scores of the possibility of implementing the MCI standards in Khorasan Razavi hospitals, were (51.6 and 12.27), respectively. CONCLUSIONS: According to half (43.8%) of managers, the MCI standards are applicable in hospitals of Khorasan Razavi; however, their application requires greater efforts by the hospitals.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".