Quality Management Systems Implementation Compared With Organizational Maturity in Hospital
Bibliographic record
Abstract
BACKGROUND: A quality management system can provide a framework for continuous improvement in order to increase the probability of customers and other stakeholders' satisfaction. The test maturity model helps organizations to assess the degree of maturity in implementing effective and sustained quality management systems; plan based on the current realities of the organization and prioritize their improvement programs. OBJECTIVES: We aim to investigate and compare the level of organizational maturity in hospitals with the status of quality management systems implementation. MATERIALS & METHODS: This analytical cross sectional study was conducted among hospital administrators and quality experts working in hospitals with over 200 beds located in Tehran. In the first step, 32 hospitals were selected and then 96 employees working in the selected hospitals were studied. The data were gathered using the implementation checklist of quality management systems and the organization maturity questionnaire derived from ISO 10014. The content validity was calculated using Lawshe method and the reliability was estimated using test - retest method and calculation of Cronbach's alpha coefficient. The descriptive and inferential statistics were used to analyze the data using SPSS 18 software. RESULTS: According to the table, the mean score of organizational maturity among hospitals in the first stage of quality management systems implementation was equal to those in the third stage and hypothesis was rejected (p-value = 0.093). In general, there is no significant difference in the organizational maturity between the first and third level hospitals (in terms of implementation of quality management systems). CONCLUSIONS: Overall, the findings of the study show that there is no significant difference in the organizational maturity between the hospitals in different levels of the quality management systems implementation and in fact, the maturity of the organizations cannot be attributed to the implementation of such systems. As a result, hospitals should make changes in the quantity and quality of quality management systems in an effort to increase organizational maturity, whereby they improve the hospital efficiency and productivity.
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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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".