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Record W4389359988 · doi:10.31274/itaa.17244

Culinary Clothing and Safety: Kitchen Uniforms as Personal Protective Equipment

2012· article· en· W4389359988 on OpenAlexaff
Briana Ehnes, Rachel H. McQueen, Megan Strickfaden

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture and Farm Safety
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPersonal protective equipmentClothingGreaseMedicinePersonal hygieneOccupational safety and healthHazardous wasteEnvironmental healthMedical emergencyToxicologyEngineeringBusinessCoronavirus disease 2019 (COVID-19)Waste management

Abstract

fetched live from OpenAlex

Chefs work long hours, often in the kitchen for 8 or more hours daily. While chefs are in this environment, they are exposed to many hazardous conditions including (but not limited to) contamination from raw meat and poultry, exposure to cleaning or pest control products, performing repetitive manual tasks, working in extreme temperatures, working with knives or other sharp equipment, risk of burns from ovens, deep fryers, steam and hot water, and slips, trips or falls (CCOHS, 2004). However, very little research has been done evaluating the effectiveness of personal protective equipment (PPE) and clothing utilized in the kitchen. While the uniform of a chef is quite standard, very little scientific evidence exists that this uniform is indeed effective in preventing against kitchen related accidents and injuries. Foodservice workers reportedly have one of the highest numbers of recordable injuries and illnesses, with the most common injuries being sprains and strains, cuts, burns and lacerations, and slips and falls (Personick, 1991). Burn injuries in food service workers were found to be mostly caused by coffee spills, grease splashes, and inadvertent bumps/contact with hot equipment (Halpin, Forst, & Zautke, 2008). Although ways to avoid these injuries was suggested, there was no mention of aprons or chef’s jackets and how they could be used to prevent against such burns. In another study, foodservice employees at one university were among a staff group that filed the highest number of accident/injury reports (Jaskolka, Andrews & Harold, 2008). Most of these injuries were due to being struck or caught by an object, slipping/tripping, and overexertion. Therefore, there is a clear need for more research to be done in regards to injury control and prevention and whether there is any potential for PPE to help lower accident rates, especially in the foodservice industry. This paper reports a study that examined the design, construction and effectiveness of the chef’s uniform as PPE in the kitchen environment.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.002
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0140.002

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 head0.015
GPT teacher head0.220
Teacher spread0.205 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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