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Record W3131911250 · doi:10.1108/ijwhm-05-2020-0084

Sandwich or long lunch? Lack of time and attendance of food outlets by French workers

2021· article· en· W3131911250 on OpenAlexaff
Camille Massey, Damien Brémaud, Laure Saulais

Bibliographic record

VenueInternational Journal of Workplace Health Management · 2021
Typearticle
Languageen
FieldMedicine
TopicObesity, Physical Activity, Diet
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsAttendanceContext (archaeology)Logistic regressionOrdered logitWageDimension (graph theory)MarketingOrder (exchange)PsychologyEnvironmental healthDemographic economicsBusinessEconomicsMedicineGeographyLabour economicsStatisticsMathematicsEconomic growth

Abstract

fetched live from OpenAlex

Purpose This study explores the relation between workers' choices of food outlets for lunch during the workday and their time constraints. Design/methodology/approach A cross-sectional survey was conducted among 1,132 French wage-earners in order to identify the dimensions indicative of lack of time among workers and to examine their associations with the likelihood of different food outlet choices. Findings Exploratory factor analysis revealed four dimensions indicative of lack of time. Binary logistic regressions revealed that each dimension was linked to at least one food outlet choice. This research suggests that the dietary practices of workers are associated with their time constraints. Practical implications Time constraints play a role in attendance of food outlets for lunch and should be taken into account when promoting healthier lunch behaviors among employees. Originality/value This is the first research investigating the links between time constraints and attendance of food outlets in the context of lunch during the workday.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.025
GPT teacher head0.336
Teacher spread0.310 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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Same venueInternational Journal of Workplace Health ManagementSame topicObesity, Physical Activity, DietFrench-language works237,207