Survey of public knowledge level on the efficacy of alcohol-based hand sanitizers
Bibliographic record
Abstract

 Introduction: Alcohol-based hand sanitizers have received wide-spread acceptance in many institutions as a form of disinfection. Whether the public truly understands the mode of action of these products and what they are effective and not effective against has not been examined. The goal of this paper is to test the public’s knowledge regarding alcohol-based hand sanitizers and examine if there are any demographic variables that may contribute to differences in knowledge level. Methods: An online survey was created via Survey Monkey and distributed through Facebook, a social media platform. A paper copy of the survey was distributed to participating senior homes in the Lower Mainland. The knowledge scores were analyzed using Microsoft Excel and NCSS to evaluate whether knowledge scores are affected by demographic variables. Incentives such as water bottles and tumblers were used to invite participants to take part in the survey. Results: The knowledge scores from respondents in health-related professions did not differ significantly from respondents in non-health related professions, however both groups differed from those that are not employed (P =0.000060). Differences in ethnicity did not result in a significantly different knowledge scores regarding hand sanitizers (P =0.441511). Respondents who are over the age of 40 (particularly those who are 70 and above) and respondents whose level of education was high school graduation or less lacked knowledge regarding hand sanitizers compared to other demographic groups. The majority of the respondents knew ABHS was effective against influenza virus. Nearly half of the respondents erroneously thought ABHS was effective against Norovirus. Conclusion: Government agencies and public health officials should focus educational efforts on the population who are over the age of 40, particularly the senior population, and whose level of education is high school or less.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".