Smoking cessation and tobacco prevention in Indigenous populations
Bibliographic record
Abstract
This article systematically reviews 91 smoking cessation and tobacco prevention studies tailored for Indigenous populations around the world, with a particular focus on Aboriginal and Torres Strait Islander populations in Australia. We identified several components of effective interventions, including the use of multifaceted programs that simultaneously address the behavioural, psychological and biochemical aspects of addiction, using resources culturally tailored for the needs of individual Indigenous populations. Pharmacotherapy for smoking cessation was effective when combined with culturally tailored behavioural interventions and health professional support, though it is generally underused in clinical practice. From a policy perspective, interventions of greater intensity, with more components, were more likely to be effective than those of lower intensity and shorter duration. For any new policy it is important to consider community capacity building, development of knowledge, and sustainability of the policy beyond guided implementation. Future research should address how the intervention can be supported into standard practice, policy, or translation into the front-line of clinical care. Investigations are also required to determine the efficacy of emerging therapies (such as e-cigarettes and the use of social media to tackle youth smoking), and under-researched interventions that hold promise based on non-Indigenous studies, such as the use of Champix. We conclude that more methodologically rigorous investigations are required to determine components of the less-successful interventions to aid future policy, practice and research initiatives.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".