Increased smoker recognition of a national quitline number following introduction of improved pack warnings: ITC Project New Zealand
Bibliographic record
Abstract
INTRODUCTION: We examined how recognition of a national quitline number changed after new health warnings were required on tobacco packaging in New Zealand (NZ). METHODS: The NZ arm of the International Tobacco Control Policy Evaluation Survey (ITC Project) is a cohort study that surveyed smokers in two waves (N = 1,376 and N = 923). Wave 1 respondents were exposed to text-based warnings with a quitline number but no wording to indicate that it was the "Quitline" number. Wave 2 respondents were exposed to pictorial health warnings (PHWs) that included the word "Quitline" beside the number as well as a cessation message featuring the Quitline number and repeating the word "Quitline." RESULTS: The introduction of the new PHWs was associated with a 24 absolute percentage point between-wave increase in Quitline number recognition (from 37% to 61%, p < .001). Recognition increased from a minority of respondents to a majority for all age groups, genders, deprivation levels (using small area and individual measures), financial stress (two measures), and ethnic groups (e.g., the level for Maori in Wave 2: 62%, Pacific peoples: 61%, and European/other: 62%). There was also an equalizing effect on previous differences in Quitline recognition by gender, ethnic group, and for both deprivation measures. DISCUSSION: This study provides some evidence for the value of clearly identifying quitline numbers on tobacco packaging as part of PHWs. While this finding is consistent with previously published studies, the finding that this intervention appeared to benefit all sociodemographic groups is novel.
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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 | 0.003 | 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 | 0.000 | 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) | 0.004 | 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".