Bibliographic record
Abstract
Introduction: The core requirement of successful and first- in- class organizations is doing right things and doing things right. Recent organizations should be able to have excellent performance from strategic and operational point of view so that they can face the current and future world challenges. Performance measurement is one of the ways for directing organization to the right targets and avoiding diversity in practices. The effective perfOlmance measurement will result in accountability and responsiveness and make it possible to maximize the utilization of limited and available resources. In this paper some of the dimensions and frameworks for performance measurement are presented and reviewed then a conceptual framework has recommended for determining performance dimensions and indicators. \n\nMethodology: This article has writed based on literature review method. \n\nLiterature review: In this paper after presenting scant history of performance measurement and characteristics of traditional models of performance measurement, following models have been introduced Balanced Scorecard in Trusts Hospitals in National Health System and The Ontario Hospitals Association, The resultants and determinants framework (RDF), Danish model, Montreal university, The experience of the "Quality Indicator Project" (QIP, USA), The NHS Performance Assessment Framework (PAF), WHO framework. Conclusion: Reviewing models and frameworks based on suggested principles indicate that every one have strengths and weakness, but in hospital PM should be considered all performance areas. So that, when performance is reviewed, theoretical and organizational features of hospital should be understood. In the end of this paper the conceptual framework is proposed that, in addition of focus on customer/patient oriented goal and strategies, measures performance of input, structural and managerial systems, output and organizational outcome and present a comprehensive and balanced picture of hospital.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".