Summary of studies assessing the economic impact of smoke-free policies in the hospitality industry - includes studies produced to 31 August 2002. Melbourne: VicHealth Centre for Tobacco Control; 2002. http://www.vctc.org.au/publ/reports/ hospitality_pape
Bibliographic record
Abstract
Well-designed studies on the economic impact of policy changes: 1. are based on objective measures; 2. use data several years before and after policy implementation; 3. use appropriate statistical analyses which test for significance, controlling for underlying trends and fluctuations in data; and 4. control for changes in economic conditions [1]. A large number of studies have examined the effect of smoke-free policies in the hospitality industry. Studies vary greatly in methodological quality. To facilitate greater analysis of methodological quality and overall trends in findings, we have compiled and summarised the publication details, key features and findings of all available studies. We attempted to locate all studies in the English language that purported to predict or assess the economic impact of smoke-free policies in the hospitality industry 1. In late November 2001, we searched Medline, Science Citation Index, Social Sciences Citation Index, Current Contents, PsychInfo, and Healthstar using the terms smok * and restaurants, bars, hospitality, economic, regulation and law. We also included unpublished studies; these studies were predominantly funded by the tobacco industry or organizations linked to the tobacco industry. These were located from a compilation by the Alberta Tobacco Control Centre [2], by a request to members of the International Union Against Cancer’s International Tobacco Control Network (GLOBALink), and an examination of hospitality industry websites and the websites of tobacco companies based in major
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.042 | 0.045 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".