Winds of change: a stocktake on progressing the Framework Convention on Tobacco Control in Indigenous contexts
Bibliographic record
Abstract
BACKGROUND: The WHO's Framework Convention on Tobacco Control (FCTC) obligates Parties to reduce tobacco use among Indigenous populations, who suffer disproportionate harm from historical and ongoing colonisation. These obligations must be upheld despite challenges like COVID and the tobacco industry's influence. AIM AND OBJECTIVES: This review updates an earlier analysis of the FCTC reports from Australia, Canada and Aotearoa New Zealand, evaluating their progress in fulfilling obligations to Indigenous peoples between 2018 and 2023. DATA SOURCES: This study employed a qualitative content analysis to review the FCTC progress reports from Australia, Canada and New Zealand, covering the period from 2018 to 2023. The analysis was based on three Global progress reports and nine country-specific reports. STUDY SELECTION AND DATA EXTRACTION: Using consistent search terms aligned with a previous review, we systematically identified relevant activities, achievements and practices reported in the FCTC documents. Two independent reviewers conducted the coding and analysis, and after initial coding, the findings were cross-checked by the research team. RESULTS: Across the three countries, there was a focus on increasing Indigenous leadership in the development and implementation of tobacco control programmes. For example, in Australia, the Tackling Indigenous Smoking programme focuses on codesigning culturally tailored interventions to address high smoking rates and overcome challenges, particularly in remote communities, while addressing gaps in providing culturally safe supports. Indigenous leadership in tobacco control was also indicated in New Zealand's Smokefree Aotearoa 2025 Action Plan and in Canada's Tobacco Strategy. However, despite the encouraging progress, absence of Indigenous-specific data and inconsistent reporting is challenging, and more work is required.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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".