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Record W3155176789 · doi:10.47339/ephj.2020.27

In an attempt at saving the environment, are you instead harming yourself?

2020· article· en· W3155176789 on OpenAlexfundvenueaboutno aff
Ravneet Athwal, Environmental Health BCIT School of Health Sciences, Helen Heacock

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Waste Reduction and Sustainability
Canadian institutionsnot available
FundersBritish Columbia Institute of Technology
KeywordsPlastic bagSanitationBusinessFood preparationSurvey researchAdvertisingEnvironmental healthFood safetyEngineeringMedicineWaste managementEnvironmental engineering

Abstract

fetched live from OpenAlex

Background With the increasing shift to reusable shopping bags and the potential ban on plastic bags in Canada in the near future, the question arises as to whether consumers are aware of the proper practices to maintain a safe environment within the bags themselves. The reason for this study was to determine if people are aware of the need to keep specific bags for certain food groups and if they are aware of the need to wash and/or sanitize their reusable shopping bags due to the risk of cross-contamination. Usage of the same bag for various foods (e.g. lettuce and raw meat) without proper sanitation practices can lead to cross-contamination between the foods, and in turn, create a risk of food borne illness. Methods A survey created on Microsoft Office 365 Word was administered through Survey Monkey and distributed on Reddit, various social media, and by email. The survey collection ran for one week in the month of January 2020. The survey consisted of 14 questions and took approximately two to three minutes to complete. Results 225 respondents filled out the online survey. The majority of survey responses were from British Columbia (47%), were female (54%), attended post-secondary institutions (65%) and were between the ages of 20 to 30 (46%). Nearly half of reusable shopping bag users use the same bag to store their fruits/vegetables and their meats, 61% of users have never cleaned their shopping bags, 7% clean them weekly, and only 1% clean their bag after every use. Those who mix produce and meats in the same bag are less likely to wash their RSBs (p = 0.0006). Males are less likely to wash their shopping bags than females (P = 0.009). 97% of survey respondents were not provided with any cleaning instructions upon their purchase of a reusable shopping bag and 93% have never seen educational material presented on RSB cleaning and/or the risk of cross-contamination. 84% believe there is not appropriate awareness and knowledge among the general public on the cleaning requirements of reusable shopping bags and the potential risk of cross-contamination while 10% believe there is sufficient awareness. Not surprisingly, those who were not aware that shopping bags need to be cleaned between uses were less likely to wash them (p = 7.804 x10-19). Conclusion In conclusion: 1. people who are not aware that their RSBs need to be cleaned between uses are also less likely to clean them, 2. males are less likely to clean their reusable shopping bags, 3. bags that contain both fruits/vegetables and meats in the same bags are also less likely to be cleaned, and 4. bags that are used more frequently also cleaned more frequently. Further education on reusable shopping bags is needed along with the transition from plastic bags to reusable shopping bags. At the time of publication, the 2020 Covid-19 pandemic was rapidly spreading throughout the world. In order to prevent fomite spread of disease, British Columbia forbade the use of RSBs in grocery stores, resulting in a proliferation of plastic bags. Time will tell when, and if, RSBs will be permitted for grocery shopping.

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)0.001
metaresearch head score (Gemma)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.257
Teacher spread0.187 · 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
Published2020
Admission routes3
Has abstractyes

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