Development of a Collaborative Decision-Making Framework to Improve the Patients' Service Quality in the Intensive Care Unit
Bibliographic record
Abstract
Healthcare is one of the biggest and complex service sectors, where the decision needs to be taken quickly, accurately and effectively. Especially service improvement decisions in Intensive Care Units (ICU) which are considered to be a predominant factor. In this study, a collaborative decision-making framework is developed to improve the Patients' Service Quality in the ICU including multiple stake-holders. The key criteria, alternatives that can advance the service quality in the ICU are identified from in-depth literature analysis. In this study, the best-worst method (BWM) is integrated with Multi-Actor Multi-Criteria Analysis (MAMCA) method to capture the stake-holder's opinion. This integrated framework allows stakeholder groups to participate in the decision-making process and to select an effective strategy to improve the patients' service quality. The result shows that hiring part-time physicians and medical staff, and hiring full-time physician and medical staff are the best solutions to improve the service quality in ICU based on physician and patient stake-holders, respectively.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".