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Record W2998885324 · doi:10.1089/fpd.2019.2754

The Effect of Food Handler Certification on Food Premises in Ontario, Canada

2020· article· en· W2998885324 on OpenAlexaffabout
Eli Rafael Barros, Wendy Pons, Ian Young, Scott A. McEwen, Andrew Papadopoulos

Bibliographic record

VenueFoodborne Pathogens and Disease · 2020
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsToronto Metropolitan UniversityConestoga CollegeUniversity of Guelph
Fundersnot available
KeywordsCertificationPremiseFood safetyOddsFood inspectionUnit (ring theory)BusinessEnvironmental healthMarketingOperations managementMedicineEngineeringLogistic regressionPsychologyManagementEconomics

Abstract

fetched live from OpenAlex

Although health units have implemented food handler certification to operators of food premises, evidence on its effectiveness to improve premise food safety remains inconclusive. Regression models were constructed using inspection data from a health unit in Ontario, Canada, to measure the effect of certification on premise inspection results. We found that premises without certified food handlers at the time of inspection were significantly more likely to fail inspections. The odds of inspection failure were significantly different depending on the premise's cultural cuisine classification. Independently owned establishments had lower odds of inspection failure versus chain operations. Inspector was a significant random effect explaining a small percentage of data variations. These results support the use of food handler certification to improve food safety outcomes at establishments. Further efforts should ensure training programs are accessible and relatable to premise operators, particularly those serving all types of cultural cuisines.

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)0.000
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 score0.838
Threshold uncertainty score0.920

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.180
Teacher spread0.161 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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