The effect of financing system on improvement of hospital performance
Bibliographic record
Abstract
Payesh2009; 8: 135-145Accepted for publication: 11 June 2007[EPub a head of print-26 May 2009] Objective(s): To investigate the effect of financing system on improvement of hospital performance in the selected countries.Methods: This is a descriptive study. The health care system of Canada, France, USA, Australia, UK, Turkey, South Korea, Norway and Iran has been studied on. The base of selection is Garden's categorizing of countries. The questionnaire of proposed model regarding implementation of the Delphi technique were filled out by 30 professionals, professors and policy makers of health care system or people with academic or work experience in hospital management. Collected data was analyzed with statistical methods. After evaluating and analyzing the proposed and corrective ideas, the final model was designed.Results: This study shows that application of financing system plays a key role in improvement of hospital performance along with comprehensive planning and appropriate control supervision system in a fundamental structure.Conclusion: The experts in this field believe that by taking into consideration the social identity of hospital; financial resources and capital goods have to be provided by the governments, training costs have to be paid separately to the hospitals and floating budgets need to be considered for 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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".