Before and after study of bar workers' perceptions of the impact of smoke-free workplace legislation in the Republic of Ireland
Bibliographic record
Abstract
OBJECTIVES: To compare support for, and perceptions of, the impacts of smoke-free workplace legislation among bar workers in the Republic of Ireland (ROI) pre- and post-implementation, and to identify predictors of support for the legislation. SETTING: Public houses (pubs) in three areas of the ROI. DESIGN: Comparisons pre- and post-implementation of smoke-free workplace legislation. PARTICIPANTS: From a largely non-random selection, 288 bar workers volunteered for the baseline survey; 220 were followed up one year later (76.4%). OUTCOME MEASURES: Level of support for the legislation, attitude statements concerning potential impacts of the law and modelled predictors of support for the legislation. RESULTS: Pre-implementation 59.5% of participants supported the legislation, increasing to 76.8% post-implementation. Support increased among smokers by 27.3 percentage points from 39.4% to 66.7% (p < 0.001) and among non-smokers by 12.4% percentage points from 68.8% to 81.2% (p = 0.003).Pre-legislation three-quarters of participants agreed that the legislation would make bars more comfortable and was needed to protect workers' health. Post-legislation these proportions increased to over 90% (p < 0.001). However, negative perceptions also increased, particularly for perceptions that the legislation has a negative impact on business (from 50.9% to 62.7%, p = 0.008) and that fewer people would visit pubs (41.8% to 62.7%, p < 0.001). After adjusting for relevant covariates, including responses to the attitude statements, support for the ban increased two to three-fold post-implementation. Regardless of their views on the economic impact, most participants agreed, both pre- and post-implementation, that the legislation was needed to protect bar workers' health. CONCLUSION: Smoke-free legislation had the support of three-quarters of a large sample of bar workers in the ROI. However, this group holds complex sets of both positive and negative perspectives on the legislation. Of particular importance is that negative economic perceptions did not diminish the widely held perception that the ban is needed to protect workers' health.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".