Non-smoking worksites in the residential construction sector: using an online forum to study perspectives and practices
Bibliographic record
Abstract
OBJECTIVES: Blue-collar workers are a recognised priority for tobacco control. Construction workers have very high smoking rates and are difficult to study and reach with interventions promoting smoke-free workplaces and cessation. The objectives of this study were to explore the smoking-related social climate in the North American residential construction sector and to identify potential barriers and facilitators to creating smoke-free worksites. METHODS: The data source used was a popular internet forum on home building. Participants included a broad and unselected population of employers, employees and freelance tradespersons working in residential construction. The forum archive contained 10 years of discourse on the subjects of smoking, workplace secondhand smoke and smoking restrictions on construction sites. Qualitative data analysis methods were used to describe major and minor discussion themes relevant to workplace smoking culture and policies in this sector. RESULTS: Participants described considerable tension between smoking and non-smoking tradespersons, but there was also much interpersonal support for cessation and support for non-smokers' rights. Employers and employees described efforts to make construction sites smoke free, and a movement towards preferential hiring of non-smoking tradespersons was discussed. Board participants wanted detailed scientific evidence on secondhand smoke exposure levels and risk thresholds, particularly in open-air workplaces. CONCLUSIONS: Experience with success of smoking bans in other challenging workplaces can be applied to the construction sector. Potential movement of smokers out of the workforce represents a challenge for public health systems to ensure equitable access to cessation supports and services.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".