Socioeconomic Status and Lifestyle Behaviours in Cancer Survivors: Smoking and Physical Activity
Bibliographic record
Abstract
Purpose: Smoking cessation and increased physical activity (PA) have been linked to better outcomes in cancer survivors. We assessed whether socioeconomic factors influence changes in those behaviours after a cancer diagnosis. Methods: As part of a cross-sectional study, a diverse group of cancer survivors at the Princess Margaret Cancer Centre (Toronto, ON), completed a questionnaire about past and current lifestyle behaviours and perceptions about the importance of those behaviours with respect to their health. The influence of socioeconomic indicators on smoking status and physical inactivity at 1 year before and after diagnosis were assessed using multivariable logistic regression with adjustment for clinico-demographic factors. Results: Of 1222 participants, 1192 completed the smoking component. Of those respondents, 15% smoked before diagnosis, and 43% of those smokers continued to smoke after. The proportion of survivors who continued to smoke increased with lower education level (p = 0.03). Of the 1106 participants answering PA questions, 39% reported being physically inactive before diagnosis, of whom 82% remained inactive afterward. Survivors with a lower education level were most likely to remain inactive after diagnosis (p = 0.003). Lower education level, household income, and occupation were associated with the perception that PA had no effect or could worsen fatigue and quality of life (p ≤ 0.0001). Conclusions: In cancer survivors, education level was a major modifier of smoking and PA behaviours. Lower socioeconomic status was associated with incorrect perceptions about PA. Targeting at-risk survivors by education level should be evaluated as a strategy in cancer survivorship programs.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".