Development of a High-reliability Model for Public Hospitals in Iran
Bibliographic record
Abstract
INTRODUCTION: As a health care organization, achieving security and high reliability is a goal in hospitals. Therefore, this research is concerned with the development of a structural high-reliability model in public hospitals of Iran.METHODOLOGY: This applied research was conducted in 2015 with a population that included directors, managers, faculty members and hospital affairs experts. A totalof 200 questionnaires were distributed in five areas of the country based on a cluster sampling method. The structural validity of the questionnaire was approved through confirmatory factor analysis test and its reliability was calculated as 0.73 by Cronbach’s alpha test. Moreover, data were analyzed by SPSS 18 and LISREL 8.5 software using factor analysis and mathematical models; and the confirmation of the model was assessed based on confirmatory factor analysis.FINDINGS: The results of factor analysis indicated that exploitable factors for factor analysis included selection of axis of reluctance to simplify interpretation, preoccupation with failure, sensitivity to operations, resilience and deference to expertise. Conversely, the ratio between Chi-Do index and degree offreedom was less than three, the RMSEA index was lower than 0.08, The NFI, NNFI, IFI and CFI values were higher than 0.9.CONCLUSIONS: By considering the axes of lack of tendency towards simplification of interpretations, concerns and tolerance against failure, sensitivity towards operations, flexibility and specialization hospitals will be able to achieve high-reliability criteria.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".