A Microcosm of Tobacco Research: An American Indian Northern Plains Review
Bibliographic record
Abstract
Objectives: Despite decades of continued commercial tobacco prevention and control efforts, smoking rates in Northern Plains American Indian (AI) communities within the United States continues to be remarkably high. We sought to take a microcosmic view of AI tobacco research in the Northern Plains region to identify the types of tobacco-related research that has been completed, and to critically examine whether the four areas of community importance outlined as best practice for tobacco programming in AI communities has been represented in the literature to date. Design: A systematic search of multiple databases was executed utilizing an established scoping review framework that was adapted to fit within an Indigenous worldview. A consequent title and abstract review of tobacco-related research published with AI Northern Plains communities was completed. Structured deductive content analysis was carried out on each article utilizing a matrix of analysis developed from existing literature on best practices in AI communities. Results: Of the 916 published studies identified, 50 met the inclusion criteria and were represented within five identified categorical themes: (1) commercial tobacco smoking as a risk factor, (2) commercial tobacco-related disease outcomes, (3) tobacco policy, (4) commercial tobacco smoking cessation, and (5) cultural or traditional tobacco use. The matrix analysis identified substantial variation in the number of studies carried out with community-identified best practices in place, including 22 of the 50 (44%) articles containing one or none of the four areas of importance noted as best practice. Conclusions: We identified a substantial lack of community and culturally informed tobacco-related research being carried out in the Northern Plains region. Community-based and culturally grounded efforts that consider colonization, historical trauma, and ACEs when planning research, funding mechanism, and health programming activities in the region are urgently needed to decrease commercial tobacco use and consequent health disparities.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.027 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".