COMPARATIVE STUDY OF ORGANIZATIONAL PERFORMANCE MEASUREMENT IN IRAN AND DEVELOPED COUNTRIES
Bibliographic record
Abstract
Background and Purpose: Organization Performance particularly in health and treatment sectors is considered as a basis for qualitative and quantitative development. Health and treatment systems also need performance improvement and measurement in order to grow and develop. This study is a comparative study of performance measurement in Iran and some developed countries with the purpose of application in the Iranian health and treatment system. Methods and Materials: In this deh1ive comparative study countries with successful experiences in measuring the performance such as Australia Canada Netherlands Sweden England and the United States were chosen. In order to collect information from these countries different sources like journals textbooks internet databases and e-mail communications were used. Collected information about these countries was summarized and classified according to intended variables and were analyzed finally in comparative tables. Results: Majority of the countries in the research had employed criteria such as credibility reliability realness timing relevance accuracy and appropriacy in their performance measurement programs and had used the findings in budgeting process. Applying the results of performance measurement has also been stressed in decision making and budgeting. Performance measurement is conducted in two dimensions in Iran: general and specific. Conclusion: The results of this study has shown that in most developed countries good performance indexes performance appraisal models and the use of performance measurement findings in decision making and budgeting are applied
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".