The double-edged relationship between COVID-19 stress andsmoking: Implications for smoking cessation
Bibliographic record
Abstract
INTRODUCTION: Although recent research shows that smokers respond differently to the COVID-19 pandemic, it offers little explanation of why some have increased their smoking, while others decreased it. In this study, we examined a possible explanation for these different responses: pandemic-related stress. METHODS: We conducted an online survey among a representative sample of Dutch current smokers from 11-18 May 2020 (n=957). During that period, COVID-19 was six weeks past the (initial) peak of cases and deaths in the Netherlands. Included in the survey were measures of how the COVID-19 pandemic had changed their smoking, if at all (no change, increased smoking, decreased smoking), and a measure of stress due to COVID-19. RESULTS: Overall, while 14.1% of smokers reported smoking less due to the COVID-19 pandemic, 18.9% of smokers reported smoking more. A multinomial logistic regression analysis revealed that there was a dose-response effect of stress: smokers who were somewhat stressed were more likely to have either increased (OR=2.37; 95% CI: 1.49-3.78) or reduced (OR=1.80; 95% CI: 1.07-3.05) their smoking. Severely stressed smokers were even more likely to have either increased (OR=3.75; 95% CI: 1.84-7.64) or reduced (OR=3.97; 95% CI: 1.70-9.28) their smoking. Thus, stress was associated with both increased and reduced smoking, independently from perceived difficulty of quitting and level of motivation to quit. CONCLUSIONS: Stress related to the COVID-19 pandemic appears to affect smokers in different ways, some smokers increase their smoking while others decrease it. While boredom and restrictions in movement might have stimulated smoking, the threat of contracting COVID-19 and becoming severely ill might have motivated others to improve their health by quitting smoking. These data highlight the importance of providing greater resources for cessation services and the importance of creating public campaigns to enhance cessation in this dramatic time.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".