Bibliographic record
Abstract
Introduction: The users' satisfaction with hospital information system (HIS) entails the progression of this system in the hospitals that the development of hospital information system and use of it result in the presentation of qualified services by the presenters of hygiene and therapy services and the reduction of treatment costs which itself results in the patients' satisfaction.In present research, the level of users' satisfaction with hospital information system was studied Doctor Gholipur and Emam Ali Hospital of Boukan city.Research method: The present study is an applied descriptive-analytical research.The research population consisted of all the employees of department of hospital information system Doctor Gholipur and Emam Ali Hospital of Boukan city The tools of collecting data were the same as the ones of similar articles questionnaire.The descriptive and analytical statistics such as One-Way Analysis of Variance and Independent T-test were used for the purpose of analyzing data.Findings: According to the research findings, the level of users' satisfaction with hospital information system in the software dimension at the average level was (40/7%), in the dimension of quality of information and data at the average level (51/5%), and in the function dimension at the average level (46/3%).Conclusion: According to the accomplished research, it can be said that the level of users' satisfaction with quality of software of hospital information system in the dimensions of software, information and data quality and function has been at the average level.But, to reach a favorable degree, a special attention should be paid to the users' expectations.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.954 | 0.954 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".