Review of: "Tobacco endgame intervention impacts on health gains and Māori:non-Māori health inequity: a simulation study of the Aotearoa-New Zealand Tobacco Action Plan"
Bibliographic record
Abstract
Building on recent progress towards the New Zealand Smokefree 2025 goal, the Government plans to introduce tobacco control legislation giving ministers powers to implement three significant new policies: a steep reduction in the number of retail outlets that can sell tobacco; a 'smokefree generation' proposal that would make it illegal to sell tobacco to anyone born after a certain date, and; regulations to remove most of the nicotine from tobacco to reduce its appeal and addictive effects.In preparation for this legislation, the Ministry of Health funded academics from Australia and New Zealand to model estimates of the likely impact of these measures, especially their contribution to achieving the Smokefree 2025 goal.The modelling, published as a preprint, Ouakrim et al. (2022) [1] ,is the subject of this review.It focuses on the modelling of the denicotinisation of tobacco because, according to the authors, it has the greatest impact.A number of significant flaws have been identified.The modelling is based on a fundamental and incorrect assumption that denicotinisation would reduce smoking by 85% over five years compared to business-as-usual.This draws on an earlier modelling paper, Wilson et al. (2022), [2] supplemented by other literature and expert opinion.The assumption, used as a key input to the model, is derived from a misinterpretation of a well-conducted randomised controlled trial of smoking cessation interventions that included very low nicotine content (VLNC) cigarettes in New Zealand in 2009-10, Walker et al. (2012).[3] The problem is that the trial design bears little relation to a population-wide denicotinisation regulatory intervention and its findings are not at all transferable to a model of the legislation.Volunteers who had already called the Quitline were given pharmacological and behavioural support;The intervention group were also given free VLNC cigarettes and instructed to smoke them if they wanted to;The trial intervention lasted only eight weeks and its impact assessed at six months.The trial does not include the most likely responses to the denicotinisation measure: switching to vaping, accessing an expanded illicit market, or workarounds by consumers or producers.
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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 | 0.013 | 0.120 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".