Associations between sociodemographic characteristics and tobacco usage in adult cancer survivors: Evidence from a population-based study
Bibliographic record
Abstract

 Background: the risk of developing new cancers persists for 15 million cancer survivors in the United States, yet many continue to engage in high-risk behaviours. This analysis aims to compare tobacco use in cancer-free respondents and cancer survivors, in order to elucidate trends and behavioural patterns associated with increased tobacco use in individuals that have survived cancer. 
 Methods: the Health Information National Trends Survey data of 2014 and 2017 was analysed for this study. Descriptive statistics were generated, and the likelihood of tobacco use was predicted using weighted logistic regression. Included in the study population were 941 cancer survivors, predominantly white (80%), 60-70 years of age, married (52%), with some level of education past high school (65%). 
 Results: the current smoking rate for cancer survivors was 12.1% versus 14.3% for those without cancer. Sub-high school education (OR 3.02, 95% CI [1.11-8.19]), separation/divorce (OR 2.71, 95% CI [1.52-4.83]), female gender, and lower household income were associated with an increased likelihood of cigarette use amongst cancer survivors. Cervical cancer (19.2%) and lymphoma (20%) survivors were most likely to smoke cigarettes compared to other cancer survivors. 
 Conclusions: this study demonstrated certain sociodemographic characteristics increase the likelihood of cigarette smoking in cancer survivors. These outcomes suggest cancer survivors with only high school education or lower, and those with household incomes of less than $35,000 are at greater risk and should be targeted for personalised tobacco cessation interventions in the future. High prevalence of smoking in cervical cancer survivors and an increased risk of tobacco-linked cancers suggests focus must be directed to interventions targeting female cancer survivors. Allocating further resources toward the at-risk populations identified in this study may reduce further morbidities in cancer survivors. 
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 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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".