Enhanced Performance and Cost-effective Clinical Engineering System for Jordan
Bibliographic record
Abstract
In Brief The article presents a comprehensive national clinical engineering medical equipment management system based on Jordan to improve equipment performance at reduced cost. The article includes the different steps involved inestablishing, implementing, and evaluating the system. The system was applied to 29 hospitals, 685 health centers, 332 dental clinics, 348 pediatrics and mother care clinics, and 23 blood banks of the Jordanian Ministry of Health. All information associated with about 20,000 main pieces of medical equipment distributed in the different ministry locations is successfully handled by the system. The system proves high efficiency compared with international figures and may present a useful guide for other countries. The article presents a comprehensive national clinical engineering medical equipment management system based on Jordan to improve equipment performance and reduced cost. The article includes the different steps involved in establishing, implementing, and evaluating the system. The system was applied to 29 hospitals, 685 health centers, 332 dental clinics, 348 pediatrics and mother care clinics, and 23 blood banks of the Jordanian Ministry of Health. All information associated with about 20,000 main pieces of medical equipment distributed in the different ministry locations are successfully handled by the system. The system proves high efficiency compared with international figures and may present a useful guide for other countries.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".