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Record W2755775000 · doi:10.47339/ephj.2016.102

Evaluating the effectiveness of alcohol-based hand sanitizers compared to alcohol-free hand sanitizers

2016· article· en· W2755775000 on OpenAlexfundvenueno aff
Derek Song, Environmental Health BCIT School of Health Sciences, Helen Heacock

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2016
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsHand sanitizerAlcoholFood scienceMicrobiologyMedicineChemistryBiology

Abstract

fetched live from OpenAlex


 Background and Purpose: Hand washing is one of the most important critical control points in public premises in preventing the spread of bacteria and viruses. There is vast research on the effectiveness of alcohol-based hand sanitizers in killing germs. However, the efficacy of alcohol-free hand sanitizers lacks real-world evidence. With little to no guidelines in which one type of hand sanitizers may be more appropriate depending on the types of public premise such as food establishments, hospitals, work place, or schools, Environmental Health Officers(EHOs)/ Public Health Inspectors(PHIs) will need to educate the public and operators on the effectiveness of these hand sanitizers and their advantages and disadvantages. The purpose of the study was to compare the effectiveness of alcohol-based hand sanitizers and alcoholfree hand sanitizers by conducting statistical analyses of the reduction in mean E.coli counts. Methods: 60 pigskins were prepared (30 for alcohol-based hand sanitizers, 30 for alcohol-free hand sanitizers), which were inoculated with E. coli, then applied either alcohol-based hand sanitizers or alcoholfree hand sanitizers. After 48 hours of incubation for E.coli growth, E.coli was counted. The difference in mean E.coli counts before applying hand sanitizers and after hand sanitizers was calculated, then compared between the two hand sanitizers. Results: The mean E.coli reduction count (CFU) from alcohol-based hand sanitizers (30 samples) was 10.200; the median was 11; the standard deviation was 1.7889; the range was 5.0000. The mean E.coli reduction count (CFU) from alcohol-free hand sanitizers (30 samples) was 10.233; the median was 10.5; the standard deviation was 0.8976; the range was 3.0000. The statistical t-test resulted in p-value of 0.1034. Conclusion: There was no significant difference between the two types of hand sanitizers. Both the alcohol-based hand sanitizers and alcohol-free hand sanitizers effectively reduced the number of E.coli counts (CFU) by averages of 10.2000 (92.7% reduction) and 10.2333 (93.03% reduction) respectively. While the BC Centre for Disease Control recommends 60 percent alcohol hand sanitizers to prevent the spread of germs, this research showed that alcohol-free hand sanitizers with sulfactants, allantoin, and benzalkonium chloride (SAB) formula is just as effective in killing germs. Therefore, EHOs/PHIs can educate the public and operators on the advantages and disadvantages on the two types of hand sanitizers in preventing the spread germs during the flu season and give practical advice or guidance on which type of hand sanitizers would be most appropriate in restaurants for example.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.649
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.119
GPT teacher head0.387
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2016
Admission routes2
Has abstractyes

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