Developing and validating a checklist for accreditation in leadership and management of hospitals in Iran
Bibliographic record
Abstract
INTRODUCTION: In the Iranian Accreditation System, leadership and management standards have been almost ignored and not paid enough and necessary attention to the structural components and the infrastructures standards in management and leadership sections. Governing body, medical staff, chief executive officer (CEO), and nursing management standards are inadequate and lack accountability. These standards could lead to reform and finally provide the context for accomplishment of an appropriate accreditation program. MATERIALS AND METHODS: This is a descriptive, comparative, and qualitative study. It was done in two phases. The first phase included literature review of the standards of the selected countries followed by comparison of the standards of the board of trustees, medical staff, CEOs, and nursing management standards to develop the primary framework for Iranian hospitals. In phase two, the primary framework was validated true three rounds of Delphi technique. RESULTS: Surveying the accreditation system standards in selected countries included the USA, Egypt, Malaysia, and Iran. It was found that the management and leadership standards were classify as governing body, medical staff, CEOs, and nursing management standards. The result of this study provides a framework for improvement of the Iranian national accreditation program. CONCLUSION: In regarded to the importance of the leadership and management standards in reform and change and promotion of the health services quality, efficiency, and effectiveness, the results of this study showed that the present standards of the Iranian accreditation assessment system and guidelines lack the necessary infrastructures for implementing a successful national accreditation program.
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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 | 0.058 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".