Self-reported depression and anxiety and healthcareprofessional interactions regarding smoking cessation andnicotine vaping: Findings from 2018 International TobaccoControl Four Country Smoking and Vaping (ITC 4CV) Survey
Bibliographic record
Abstract
INTRODUCTION: People with mental health conditions are disproportionately affected by smoking-related diseases and death. The aim of this study was to assess whether health professional (HP) interactions regarding smoking cessation and nicotine vaping products (NVPs) differ by mental health condition. METHODS: The cross-sectional 2018 International Tobacco Control Four Country (Australia, Canada, England, United States) Smoking and Vaping Survey data included 11040 adults currently smoking or recently quit. Adjusted weighted logistic regressions examined associations between mental health (self-reported current depression and/or anxiety) and visiting a HP in last 18 months; receiving advice to quit smoking; discussing NVPs with a HP; and receiving a recommendation to use NVPs. RESULTS: Overall, 16.1% self-reported depression and anxiety, 7.6% depression only, and 6.6% anxiety only. Compared with respondents with no depression/anxiety, those with depression (84.7%, AOR=2.65; 95% CI: 2.17-3.27), anxiety (82.2%, AOR=2.08; 95% CI: 1.70-2.57), and depression and anxiety (87.6%, AOR=3.74; 95% CI: 3.19-4.40) were more likely to have visited a HP. Among those who had visited a HP, 47.9% received advice to quit smoking, which was more likely among respondents with depression (AOR=1.58; 95% CI: 1.34-1.86), and NVP discussions were more likely among those with depression and anxiety (AOR=1.63; 95% CI: 1.29-2.06). Of the 6.1% who discussed NVPs, 33.5% received a recommendation to use them, with no difference by mental health. CONCLUSIONS: People with anxiety and/or depression who smoke were more likely to visit a HP than those without, but only those with depression were more likely to receive cessation advice, and only those with depression and anxiety were more likely to discuss NVPs. There are missed opportunities for HPs to deliver cessation advice. NVP discussions and receiving a positive recommendation to use them were rare overall.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".