Impact of immigration status on health behaviors and perceptions in cancer survivors
Bibliographic record
Abstract
Abstract Background Health behaviors including smoking cessation, physical activity (PA), and alcohol moderation are key aspects of cancer survivorship. Immigrants may have unique survivorship needs. We evaluated whether immigrant cancer survivors had health behaviors and perceptions that were distinct from native‐born cancer survivors. Methods Adult cancer patients from Princess Margaret Cancer Centre were surveyed on their smoking, PA, and alcohol habits and perceptions of the effects of these behaviors on quality of life (QoL), 5‐year survival, and fatigue. Multivariable models evaluated the association of immigration status and region‐of‐origin on behaviors and perceptions. Results Of the 784 patients, 39% self‐identified as immigrants. Median time of survey was 24 months after histological diagnosis. At baseline, immigrants had trends toward not meeting Canadian PA guidelines or being ever‐drinkers; patients from non‐Western countries were less likely to smoke (aORcurrent = 0.46, aORex‐smoker = 0.47, P = 0.02), drink alcohol (aORcurrent = 0.22, aORex‐drinker = 0.52, P < 0.001), or meet PA guidelines (aOR = 0.44, P = 0.006). Among immigrants, remote immigrants (migrated ≥40 years ago) were more likely to be consuming alcohol at diagnosis (aOR = 5.70, P < 0.001) compared to recent immigrants. Compared to nonimmigrants, immigrants were less likely to perceive smoking as harmful on QoL (aOR = 0.58, P = 0.008) and survival (aOR = 0.56, P = 0.002), and less likely to perceive that PA improved fatigue (aOR = 0.62, P = 0.04) and survival (aOR = 0.64, P = 0.08). Conclusions Immigrants had different patterns of health behaviors than nonimmigrants. Immigrants were less likely to perceive continued smoking as harmful and were less likely to be aware of PA benefits. Culturally tailored counselling may be required for immigrants who smoke or are physically sedentary at diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".