Integrating industry 4.0 into hospital waste management (HWM4.0): a framework and application of the novel interval CoCoSo method
Bibliographic record
Abstract
Effective management of hospital waste (HWM) is vital to improving healthcare quality; especially when it is aligned with 4.0 technologies. This empirical study intends to create a framework for the incorporation of HWM and Industry 4.0 technologies; referred to as HWM 4.0 in this study. The originality of this paper can be defined into two parts. First, to conceptualize and define the HWM 4.0 framework. Second, the novel application of a Combinatorial-Interval Compromise Solution (CoCoSo) method, specifically developed in this study to address interval-valued uncertainty in expert judgments, representing a methodological contribution introduced for the first time within the HWM 4.0 context. To help accomplish the aims of this empirical study, the study first establishes and classifies HWM activities. After which, it evaluates the role of Industry 4.0 technologies for these categorized activities. HWM activities are classified into the seven domains of control activities within hospitals; human resources, infrastructure and equipment, financial control activities, operations and processes, energy use and waste reduction, information systems and technology, and biological risk control activities and safety. The study applies Combinatorial-Interval CoCoSo method in two case studies on eight hospitals. Hospitals are prioritized based on control activities within hospitals. Finally, the paper provides a framework for actions and evaluation of HWM 4.0, highlighting that it is a significant enhancement on previous paradigms.
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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 | 0.000 | 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".