A life-cycle cost analysis of resilient flooring materials in acute-care facilities
Bibliographic record
Abstract
Objective: This study compares the impact of maintenance protocols on coated and non-coated resilient flooring materials over the building life of an acute-care facility. The purpose of this study is to provide healthcare administrators, facility managers and designers with evidence regarding the total cost of ownership of different resilient flooring materials.Methods: Utilizing a life-cycle costing analysis (LCCA), a two-phase economic evaluation was conducted using both industry and real-time data collected from four health systems across three distinct geographic regions in the U.S. to evaluate the impact of coated and non-coated resilient flooring materials over the usable life of an acute-care facility.Results: Findings from both the first and second phase LCCA suggest that maintenance protocols can have a substantive impact on the total cost of ownership for resilient flooring materials due to the increase in operations and maintenance costs associated with a coated maintenance protocol. The point in time at which the factory applied finish failed for a non-coated flooring material was also shown to greatly contribute to the total cost of ownership.Conclusions: The use of real-time data, coupled with a systematic evaluation provided contextual information that proved essential to understanding some of the intricacies involved in resilient flooring maintenance protocols that can greatly influence economic outcomes. This approach supports an evidence-based decision making process for healthcare executives and environmental services staff to not only effectively evaluate new resilient flooring material selections, but to also proactively evaluate current maintenance protocols for increased monetary savings.
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".