An evaluation of conventional cleaning and disinfection and electrostatic disinfectant spraying in K-12 schools
Bibliographic record
Abstract
Background: Microbes endemic to student desks can survive for long periods and infect students. The effectiveness of conventional cleaning and disinfection practices and electrostatic disinfectant spraying were examined. Methods: Six K-12 schools in Southeastern Ontario participated in the study. The viable microbial loads on 100 student desks were assessed via Replicate Organism Detection and Counting (RODAC) plates before and after cleaning and disinfection procedures. Results: The adjunctive effect of electrostatic disinfectant spraying was tested on 36 desks. Mean pretest colony-forming units (CFUs) per desk were 126.8 (SD 95.7), after conventional cleaning and disinfection mean CFUs were 73.4 (SD 93.0) (t = 4.0, P = 0.0003), and subsequent electrostatic disinfectant spraying further reduced mean CFUs to 54.2 (SD 85.0) (t = 2.6, P = 0.02). The independent effect of electrostatic disinfectant spraying without an intervening conventional cleaning step was tested on 64 desks. Mean pretest CFUs were 106.4 (SD 94.5) and after electrostatic disinfectant spraying mean CFUs decreased to 62.9 (SD 87.1) (t = 3.3, P = 0.001). Conclusions: Conventional and electrostatic disinfection methods were both effective in increasing the hygienic state of student desks. Electrostatic disinfection spraying improved hygienic state when conducted after conventional cleaning and disinfection and when used independently.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".