AComparative study of the organizational superiority model in Health care at selected countries proposing a model for Iran
Bibliographic record
Abstract
Introduction: Quality has a sixty years old historical heritage in health care establishment. Most countries use organizational superiority models an approach to execute total quality management, which in turn would lead to improved work performance and drastic change in organizations. The present study is to view the organizational superiority model in selected countries and come up with a model for Iran. Methods: This cross-sectional comparative study was conducted in 1381-82 (Iranian calendar) 2003- 2004, in order to design a superiority model of organization for Iranian Health care sector. Data collection was done in two stages using a questionnaire. The validity and reliability of the questionnaires was evaluated by comparing the superiority model of organizations in America, England, Canada, and Australia a suitable model was designed for Iran. This model was put in to test by 25 experts using Delfi model, and then the final model was eventually ready to be presented. Findings: The highest score in American model was given to the role of "leadership" in English model to "client, feed back" and in Australian model to "leader ship and innovation" and in Canadian model to "employee focus". The main criteria in the final model after applying Delfi test were: leadership, Strategic planning, "client approach, corporate culture, data analysis, Processes, resources and organizational performance. Results: Strengthening administrators, enriching corporate culture, promoting creativity and learning, rendering services based on need assessment and client's expectations, along with the client's satisfaction should be taken into consideration.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".