Comparison of Hospital Accreditation Standards in Selected Worlds: A Comparative Review
Bibliographic record
Abstract
Background: Accreditation of hospitals plays a significant role in increasing the safety, quality and effectiveness of medical services and increasing the efficiency of hospitals. This study has been conducted with the aim of investigating and comparing the international accreditation standards of the United States, Canada, Australia and France with the national accreditation system of Iran. Methods: This study examined the accreditation standards of five countries, United States, Canada, Australia, France and Iran, as a comparative study using a six-steps protocol in 2024. Embase, PubMed/MEDLINE, ISI/Web of Science (WOS), Scopus and Iranian databases such as MagIran, SID and Irandoc were searched from 2017 to 2023. Ritchie's framework analysis method was used for data analysis. Results: The review and comparison of Iran's national accreditation standards with the international accreditation standards of USA, Canada, Australia and France showed that USA, having 304 standards and 1218 measures and covering clinical, managerial and organizational dimensions and education and research, has the most complete and comprehensive standards. In the accreditation standards of the United States, Canada, France and Australia, there is a joint emphasis on improving safety, clinical effectiveness, consumer information, staff development, accountability and patient and community participation. This is while in Iran, the emphasis is on creating facilities and basic information and checking the competence in human and technical resources. The percentage of compliance of Iran's accreditation standards with American, Canadian, Australian and French accreditation standards is 50%, 54%, 57% and 45%, respectively. Conclusion: Amendments should be made in Iran's accreditation system in order to improve the content of the standards. In order to improve its effectiveness, Iran's accreditation needs the transparency of standards and measures, specific classification and grouping for standards, the use of a comprehensive view in developing standards, and changing the scoring scale of measures.
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".