Enhancing Hospital Service Quality and Patient Safety through the MIRACLE Model: A Partial Least Squares Equation Approach
Bibliographic record
Abstract
Objective: Give a background why this model is worthwhile by briefly highlighting the current health system and how this study may help to improve this system. This study aims to assess the impact of the MIRACLE model onquality enhancement and patient safety within healthcare settings. Study Design: Employing a cross-sectional design, this research centers on four key variables and 19 corresponding indicators. Data were collected using a questionnaire distributed via Google Forms, targeting heads of work units at M. Djamil Central General Hospital in Padang, Indonesia. Method: The analysis utilizes the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique to evaluate variable relationships. The variables studied are Communitarian, Apprenticing Affinity, Managing, and Quality and Patient Safety, with indicators integrated into the questionnaire. Results: Communitarian and Apprenticing Affinity emerged as influential factors directly impacting quality of health servicesand patient safety, moderated by Managing variable. Research findings reveal a significant positive impact of the Apprenticing Affinity variable on Managing (p-value = 0.013), underlining its significance in hospital management. Moreover, Apprenticing Affinity significantly affects quality and patient safety (p-value = 0.00), highlighting its pivotal role in healthcare enhancement. Similarly, the Communitarian variable significantly influences Managing (p-value = 0.11), notably impacting quality and patient safety (p-value = 0.00). However, Managing alone does not significantly impact quality and patient safety (p-value = 0.15). Indirectly, the research unveils that the Managing-moderated Apprenticing Affinity variable lacks significant influence on quality and patient safety (p-value = 0.268). Similarly, Managing-moderated Communitarian influence does not substantially impact quality and patient safety (p-value = 0.411). Conclusion: This study highlights the substantial impact of Communitarian and Apprenticing Affinity, moderated by Managing, on quality and patient safety. Notably, Managing alone lacks direct influence. These findings underscore the significance of cultivating collaborative, learning-oriented environments, alongside effective management practices, to bolster healthcare quality and patient safety.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".