Determinants of smoke-free homes adoption among Spanish adults who smoke: A prospective cohort study from the 2016–2021 International Tobacco Control (ITC) EUREST-PLUS Spain Surveys
Bibliographic record
Abstract
OBJECTIVE: To assess the prevalence and associated factors of smoke-free homes (SFHs) among Spanish adults who smoke across three cohort waves, and to identify determinants of SFH adoption during follow-up (2016-2021). METHODS: The International Tobacco Control EUREST-PLUS Spain Survey is a nationally representative cohort of ∼1000 adults (≥18 years) who smoke surveyed in 2016, 2018, and 2021. First, we conducted repeated cross-sectional analysis to estimate the prevalence of SFHs at each wave. Second, we estimated incidence and risk ratios (RR) with 95 % confidence intervals (CI) for SFH adoption during the follow-up using adjusted generalised linear models. Independent variables included sociodemographics, smoking characteristics, and beliefs about second-hand smoke harms. RESULTS: SFH prevalence was 13.1 % in 2016, 19.0 % in 2018, and 31.5 % in 2021 (p trend <0.001). Quitting smoking (RR = 2.66; 95 % CI: 2.10, 3.36), remaining in any stage other than precontemplation (RR = 1.76; 1.13, 2.73) and progressing beyond precontemplation stage (RR = 2.59; 1.99, 3.37) were determinants of SFH adoption. Maintaining moderate or high nicotine dependence (RR = 0.46; 0.30, 0.69) was inversely associated with SFH adoption. CONCLUSIONS: SFH prevalence among Spanish adults who smoke increased in 2016-2021. Initiatives promoting SFHs should encourage progression through the stages of change towards cessation and provide tailored support for individuals with high nicotine dependence.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".