Impact of antimicrobial copper surfaces on microbial load and healthcare-acquired infection rates in long-term care settings: A comparative study in British Columbia, Canada
Bibliographic record
Abstract
Background: Healthcare-acquired infections (HAIs) pose significant challenges in healthcare facilities. Although antimicrobial copper surfaces have shown promise in reducing environmental microbial contamination, their effectiveness specifically in long-term care (LTC) homes remains insufficiently explored. This study aims to evaluate the impact of antimicrobial copper surfaces on microbial load and HAI rates in the LTC home setting. Methods: A prospective study was conducted across units of three LTC homes over six months. Antimicrobial copper was installed on designated common surfaces in intervention units, while control units retained existing surfaces. Microbial load was assessed weekly using Hygiena® SuperSnap® ATP bioluminescence assay and 3M™ Petrifilm™ Aerobic Count culture plates. HAI rates were monitored in two facilities over the same period. Results: The study revealed a substantial reduction in microbial load on copper surfaces compared to conventional surfaces, with reductions of 79.3% and 34.1% using ATP bioluminescence and aerobic microbial culture methods, respectively. HAI rates did not significantly differ between intervention and control units. Of the 30 recorded cases of HAI during the study period, 70% occurred during respiratory infection outbreaks, with 12 cases in intervention units and nine in control units. Conclusion: Antimicrobial copper surfaces show potential for reducing microbial contamination in LTC homes. However, further research is needed to comprehensively assess their impact on HAI rates in this setting.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 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".