The effect of coverage of smoking‐cessation aids on tobacco use: Evidence from Canada
Bibliographic record
Abstract
In clinical trials, smoking-cessation aids (SCAs) have proven to be effective at improving the odds of smoking cessation. Because of the effectiveness of SCAs in these settings, many countries have adopted the coverage of SCAs to reduce tobacco use. However, the effect of such coverage on tobacco use is ambiguous. On one hand, the coverage may have the intended effect and reduce tobacco use. On the other hand, the coverage may cause beneficiaries to participate in tobacco use more as the drug coverage protects beneficiaries from future costs associated with tobacco use. To understand the effect of SCA coverage, we examine it using 2008-2012 Canadian Tobacco Use Monitoring Survey and a difference-in-differences approach. We find that SCA coverage increases cigarette and cigarillo use. Moreover, the effect of SCA coverage on tobacco use is stronger in men and in those with at least a college education. Our results point to the unintended consequences of the coverage of SCAs on tobacco use.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".