The Intersection of Food Insecurity and Tobacco Use: A Scoping Review
Bibliographic record
Abstract
Cigarette smoking is increasingly concentrated in socioeconomically disadvantaged groups, and food insecurity also disproportionately affects lower-income groups. Recent studies have suggested that smoking and food insecurity operate as risk factors for one another, but there is limited understanding of their intersection. This scoping review aimed to synthesize the published literature on the association between food insecurity and tobacco use across population groups in the United States and Canada. We searched PubMed, Web of Science, and PsycINFO using key words. Studies included were published in English between 2008 and 2018, reported empirical findings, measured both tobacco use and food insecurity, and considered either variable as a study outcome. Nineteen articles were identified; 6 examined tobacco use as an outcome variable and 13 examined food insecurity as an outcome variable. Most articles were of studies using cross-sectional designs. Study samples ranged from general populations, clinical samples, and underserved populations. For each article, we extracted information including specific findings related to the association between food insecurity and tobacco use. We synthesized the current research by formulating a model by which food insecurity and tobacco use are bidirectionally associated. This scoping review concludes that the co-occurrence of food insecurity and tobacco use exists across populations in the United States and Canada. As the evidence is largely from cross-sectional investigations, there is a need for longer term, comprehensive assessments of relationships between tobacco use and food insecurity. Such investigations can inform policies and interventions aimed toward addressing the inequitable burden of tobacco use and of food insecurity among disadvantaged populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.020 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".