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Record W4293093134 · doi:10.47339/ephj.2021.195

A survey study of public awareness on hand sanitizer recalls in Canada

2022· article· en· W4293093134 on OpenAlexvenueaboutno aff
Ming Mei Yang, Helen Heacock

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsnot available
Fundersnot available
KeywordsHand sanitizerNoticePandemicBusinessPublic healthEconomic shortageCoronavirus disease 2019 (COVID-19)Hand washingEnvironmental healthMedicineHygienePolitical scienceGovernment (linguistics)Infectious disease (medical specialty)

Abstract

fetched live from OpenAlex

The Covid-19 pandemic has created a high demand for hand sanitizer to prevent the spread of pathogens. This resulted in a hand sanitizer shortage on the market and promoted more manufactures to produce and sell hand sanitizers. Meanwhile, Health Canada has recalled many hand sanitizers due to potential health risks for various reasons such as products that contain unauthorized ingredients. However, the general public in Canada might not notice hand sanitizer recalls and/or underestimate the risk of using recalled hand sanitizers. This research study was conducted to determine the general public awareness regarding hand sanitizer recalls.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.158

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.111
GPT teacher head0.322
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2022
Admission routes2
Has abstractyes

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