Tobacco use, trauma exposure and PTSD: a systematic review
Bibliographic record
Abstract
Tobacco use remains one of the most significant preventable public health problems globally and is increasingly concentrated among vulnerable groups, including those with trauma exposure or diagnosed with PTSD. The goal of this systematic review was to update and extend previous reviews. Of the 7224 publications that met the initial criteria, 267 were included in the review. Summary topic areas include conceptual frameworks for the relation between trauma or PTSD and tobacco use; associations between trauma exposure or PTSD and tobacco use; number and type of trauma exposures and tobacco use; PTSD symptoms and tobacco use; Treatment-related studies; and the examination of causal relations. Evidence continues to indicate that individuals exposed to trauma or diagnosed with PTSD are more likely to use tobacco products, more nicotine dependent and less likely to abstain from tobacco even when provided evidence-based treatments than individuals without trauma. The most commonly cited causal association proposed was use of tobacco for self-regulation of negative affect associated with trauma. A small proportion of the studies addressed causality and mechanisms of action. Future work should incorporate methodological approaches and measures from which we can draw causal conclusions and mechanisms to support the development of viable therapeutic targets.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".