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Record W4386609166 · doi:10.4315/fpt-21-039

Identifying Predictors of Safe Food Handling Practices among Canadian Households with Children Under Eighteen Years

2023· article· en· W4386609166 on OpenAlexaboutno aff
David Obande

Bibliographic record

VenueFood Protection Trends · 2023
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFood Safety and Hygiene
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmental healthHygieneFood safetyMedicinePsychological interventionFood preparationBachelorGeographyNursing

Abstract

fetched live from OpenAlex

Poor food handling practices at home are a common cause of foodborne illness. Children are more susceptible to foodborne illness than adults. Because children’s food safety depends on the safe food handling practices of parents and caregivers, this study aims to identify determinants of safe food handling practices among Canadian families with children under 18 years. Data for Canadian households with children (n = 294) were extracted from a larger telephone survey conducted across all Canadian provinces and territories between 2014 and 2015. Four food safety practice outcomes and six demographic variables were examined using multivariable logistics regression. Most survey participants were females (56%) who had less than a bachelor’s degree (67%) and were caring for one child (55%). Approximately 90% of caregivers reported proper hand hygiene, and 79% refrigerated leftovers within 2 h of cooking. Only 33% of caregivers reported preventing cross-contamination, and fewer reported using food thermometers for poultry cuts (13%) and hamburgers (11%). Those in the higher income and education categories were less likely to follow safe food handling practices such as hand hygiene and safe refrigeration of leftovers. This research highlights the need for food safety interventions that target Canadian families with children within certain demographic groups.

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 score0.509
Threshold uncertainty score0.964

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.052
GPT teacher head0.231
Teacher spread0.179 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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