Impact of Canadian tobacco packaging policy on quitline reach and reach equity
Bibliographic record
Abstract
OBJECTIVE: To examine the impact of the new Canadian tobacco package warning labels with a quitline toll-free phone number for seven provincial quitlines, focusing on treatment reach and reach equity in selected vulnerable groups. METHODS: A quasi-experimental design assessed changes in new incoming caller characteristics, treatment reach for selected vulnerable sub-populations and the extent to which this reach is equitable, before and after the introduction of the labels in June, 2012. Administrative call data on smokers were collected at intake. Pre- and post-label treatment reach and reach equity differences were analysed by comparing the natural logarithms of the reach and reach equity statistics. RESULTS: During the six months following the introduction of the new warning labels, 86.4% of incoming new callers indicated seeing the quitline number on the labels. Treatment reach for the six-month period significantly improved compared to the same six-month period the year before from .042% to .114% (p<.0001) and reach equity significantly improved for young males (p<.0001) and those with high school education or less (p=.004). CONCLUSIONS: The introduction of the new tobacco warning labels with a quitline toll-free number in Canada was associated with an increase in treatment reach. The toll-free number on tobacco warning labels aided in reducing tobacco related inequalities, such as improved reach equity for young males and those with high school or less education.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".