A life-cycle cost analysis for flooring materials for healthcare facilities
Bibliographic record
Abstract
Objective: In this study, hard, resilient and soft flooring materials are compared using a building service life of 50 years, an established life span for healthcare facilities. The purpose of this study is to evaluate the life-cycle cost of flooring products and inform decision-makers about the long-term cost of ownership along with other key factors, such as safety, durability, and aesthetics.Methods: The protocol for executing an life-cycle cost analysis (LCCA) is defined by the National Institute of Standards and Technology (NIST), including defining the problem, identifying feasible alternatives, and establishing common assumptions and parameters, as well as acquiring financial information. Product information for the flooring materials that met inclusion criteria based on characteristics of the products consistent with use in healthcare facilities was acquired including maintenance, installation, warranty, and cost data. LCCA calculations recognize the time value of money and use discounting to project future value.Results: The results generated from the LCCA using present value to project future costs provide a useful tool for projecting costs over time for the purpose of planning operational and maintenance costs associated with the long-term investment of ownership. The findings suggest that soft flooring is more cost effective for initial purchase and installation, equipment assets, and maintenance over time of facilities.Conclusions: Cost is an important factor when considering flooring materials for healthcare facilities. Other factors to be considered are safety, durability and aesthetics, cleanliness, acoustics and sustainability to realize the overall return on investment. This study developed a tool for projecting costs of ownership for facility materials, in this case, flooring. The selection of flooring material has a significant impact on the cost of ownership over the life of the facility.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".