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Record W2100106775 · doi:10.1136/tc.10.1.33

Actual and perceived impacts of tobacco regulation on restaurants and firms

2001· article· en· W2100106775 on OpenAlexafffundabout
Pierre‐Yves Crémieux, Pierre Ouellette

Bibliographic record

VenueTobacco Control · 2001
Typearticle
Languageen
FieldMedicine
TopicSmoking Behavior and Cessation
Canadian institutionsUniversité du Québec à Montréal
FundersMinistère de la SantéMinistère de la Santé et des Services sociaux
KeywordsRevenueAbsenteeismProductivityBusinessWork (physics)Tobacco industryChristian ministryEconomic costCost–benefit analysisIndirect costsPublic economicsFinanceEconomicsEconomic growthAccountingMedicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To examine the actual and anticipated costs of a law regulating workplace smoking and smoking in restaurants, taking into consideration observed and anticipated infrastructure costs, lost productivity, increased absenteeism, and loss of clientele. SETTING AND DESIGN: A survey of 401 Québec restaurants and 600 Québec firms conducted by the Québec Ministry of Health before the enactment of the law was used to derive costs incurred by those who had already complied and anticipated by those that did not. RESULTS: Direct and indirect costs associated with tobacco regulation at work and in restaurants were minimal. Annualised infrastructure costs amounted to less than 0.0002% of firm revenues and 0.15% of restaurant revenues. Anticipated costs were larger and amounted to 0.0004% of firm revenues and 0.41% of restaurant revenues. Impacts on productivity, absenteeism, and restaurant patronage were widely anticipated but not observed in currently compliant establishments. CONCLUSION: Firms and restaurants expected high costs to result from strict tobacco regulation because of infrastructure costs, decreased productivity, and decreased patronage. That none of these were actually observed suggests that policy makers should discount industry claims that smoking regulations impose undue economic hardship.

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 score0.058
Threshold uncertainty score0.359

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 head0.283
Teacher spread0.263 · 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

Citations35
Published2001
Admission routes3
Has abstractyes

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