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Record W2155940705 · doi:10.1002/ajim.20475

Occupational injury among cooks and food service workers in the healthcare sector

2007· article· en· W2155940705 on OpenAlexaff
Hasanat Alamgir, Helena Swinkels, Shicheng Yu, Annalee Yassi

Bibliographic record

VenueAmerican Journal of Industrial Medicine · 2007
Typearticle
Languageen
FieldMedicine
TopicOccupational exposure and asthma
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicinePoisson regressionOccupational safety and healthAllergyOccupational medicineEnvironmental healthHealth careMedical emergencyGerontologyOccupational exposurePopulation

Abstract

fetched live from OpenAlex

BACKGROUND: Incidence of occupational injury is anticipated to be high among cooks and food service workers (CFSWs) because of the nature of their work and the types of raw and finished materials that they handle. METHOD: Incidents of occupational injury, resulting in lost time or medical care over a period of 1 year in two health regions were extracted from a standardized operational database and with person years obtained from payroll data, detailed analysis was conducted using Poisson regression modeling. RESULTS: Among the CFSWs the annual injury rate was 38.1 per 100 person years. The risk of contusions [RR, 95% CI 9.66 (1.04, 89.72)], burns [1.79 (1.39, 2.31)], and irritations or allergies [3.84 (2.05, 7.18)] was found to be significantly higher in acute care facilities compared to long-term care facilities. Lower risk was found among older workers for irritations or allergies. Female CFSWs, compared to their male counterparts, were respectively 8 and 20 times more likely to report irritations or allergies and contusions. In respect to outcome, almost all irritations or allergies required medical visits. For MSI incidents, about 67.4% resulted in time-loss from work. CONCLUSIONS: Prevention policies should be developed to reduce the hazards present in the workplace to promote safer work practices for cooks and food service workers.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.106
Threshold uncertainty score0.333

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.050
GPT teacher head0.329
Teacher spread0.280 · 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.

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

Citations36
Published2007
Admission routes1
Has abstractyes

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