Use of a national quitline and variation in use by smoker characteristics: ITC Project New Zealand
Bibliographic record
Abstract
INTRODUCTION: We aimed to describe use of a national quitline service and the variation in its use by smoker characteristics (particularly ethnicity and deprivation). The setting was New Zealand (NZ), which takes proactive measures to attract disadvantaged smokers to this service. METHODS: The NZ arm of the International Tobacco Control Policy Evaluation Survey (ITC Project) utilizes the New Zealand Health Survey (a national sample) from which we surveyed adult smokers in two waves (N = 1,376 and N = 923) 1 year apart. RESULTS: Quitline use in the last 12 months rose from 8.1% (95% CI = 6.3%-9.8%) in Wave 1 to 11.2% (95% CI = 8.4%-14.0%) at Wave 2. Māori (the indigenous people of NZ) were significantly more likely to call the Quitline than were European/other smokers. Relatively higher call rates also occurred among those reporting higher deprivation, financial stress, a past mental health disorder, a past drug-related disorder, and higher psychological distress (Kessler 10-item index). Independent associations in the multivariate analyses of Quitline use were being Māori, reporting financial stress, and ever having been diagnosed with a mental health disorder. DISCUSSION: This national Quitline service is successfully stimulating disproportionately more calls by Māori smokers and those with some measures of disadvantage. It may therefore be contributing to reducing health inequalities. It appears possible to target quitlines to reach those smokers in greatest need.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".