Socioeconomic status and lifestyle behaviors in cancer survivors.
Bibliographic record
Abstract
6568 Background: Socioeconomic disparities in cancer survival exist even in universal healthcare systems. These disparities may be explained in part by lifestyle behaviors such as smoking and physical activity (PA). We evaluated the associations between socioeconomic variables and changes in smoking and PA after diagnosis in Ontario cancer survivors. Methods: 1,252 adult cancer survivors across diverse disease sites were surveyed about their smoking and exercise habits. Using multivariate logistic regression models, we evaluated the association of income, occupation, and education with each behavior, after adjusting for clinicodemographic and pathological covariates. Results: Cancer survivors were surveyed at a median of 26 months after diagnosis. 16% had breast cancers, 12% gastrointestinal, 26% gynecological/genitourinary, 14% head and neck, 6% lung and 19% hematologic. 15% reported being smokers at diagnosis; 45% reported being physically inactive; after diagnosis, 56% had quit smoking and 18% increased their PA. Survivors with a lower education level were more likely to be current smokers (p<0.0001) and less likely to quit if they were smoking at diagnosis (p=0.02). Similarly, patients with less education were more likely to be physically inactive currently (p<0.0001), and less likely to improve if they were inactive when they were diagnosed (p=0.004). In contrast, household income and occupation were not associated with current engagement or changes in these behaviours. Population-based marginalization indices confirmed that factors related to education level were significantly associated with smoking cessation (p<0.05). Conclusions: Cancer survivors with lower educational levels were more likely to have at baseline, and maintain, after diagnosis, unhealthy lifestyle behaviors. Targeting at-risk survivors by education level should be evaluated as a strategy in cancer survivorship programs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".