Smoking Cessation in Indigenous Populations of Australia, New Zealand, Canada, and the United States: Elements of Effective Interventions
Bibliographic record
Abstract
Indigenous people throughout the world suffer a higher burden of disease than their non-indigenous counterparts contributing to disproportionate rates of disability. A significant proportion of this disability can be attributed to the adverse effects of smoking. In this paper, we aimed to identify and discuss the key elements of individual-level smoking cessation interventions in indigenous people worldwide. An integrative review of published peer-reviewed literature was conducted. Literature on smoking cessation interventions in indigenous people was identified via search of electronic databases. Documents were selected for review if they were published in a peer-reviewed journal, written in English, published from 1990-2010, and documented an individual-level intervention to assist indigenous people to quit smoking. Studies that met inclusion criteria were limited to Australia, New Zealand, Canada, and the USA, despite seeking representation from other indigenous populations. Few interventions tailored for indigenous populations were identified and the level of detail included in evaluation reports was variable. Features associated with successful interventions were integrated, flexible, community-based approaches that addressed known barriers and facilitators to quitting smoking. More tailored and targeted approaches to smoking cessation interventions for indigenous populations are required. The complexity of achieving smoking cessation is underscored as is the need to collaboratively develop interventions that are acceptable and appropriate to local populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".