Analyzing knowledge, attitudes and practices around reusable water bottles
Bibliographic record
Abstract
Background: It has been acknowledged that personal reusable water bottles pose hazards, such as disease-causing organisms, associated with poor water bottle hygiene practices. Currently, there are no recommended frequencies or procedures, or guidelines for personal water bottle cleaning and sanitation. Likewise, there is little information on outbreaks or cases of illness arising from poor personal water bottle hygiene. This may be due to lack of awareness and non-reporting of cases. Therefore, the importance of knowledge, attitudes and practices around reusable water bottles cannot be over emphasized. This research study will ascertain if water bottle hygiene practices among post-secondary education students are adequate to avoid consumption of drinking water with growth of multiple pathogenic microbes like Escherichia coli, Salmonella species, Pseudomonas species, Vibrio cholera and viruses. Method: 83 participants were surveyed using an in person administered survey method. The survey was conducted on British Columbia Institute of Technology (BCIT) student sample using the Survey Monkey platform and was delivered in person via an iPad at a survey stand. Chi-square tests were used to analyze the survey data using NCSS version 12 statistical package. Tables and bar charts were used to explain and give interpretation to p-values from the chi-square tests. Results: There were found to be no associations between knowledge level around reusable water bottles and either gender or hygiene practices. However, the survey data did show an association between gender and hygiene level. The female participants were more likely to clean their water bottles more frequently than the male participants. Conclusion: Based on the findings of this study, a health promotion initiative targeted toward male students is recommended to achieve behaviour change in cleaning practices with reusable drinking water bottles. Moreover, despite the study findings showing a high level of knowledge among participants, this did not translate to better water bottle hygiene practices, as there was no statistical association between knowledge level and hygiene practice. Therefore, more frequent cleaning of reusable water bottles should be encouraged, highlighting the appropriate cleaning agents and method to be used.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".