Making Lifestyle Changes after Colorectal Cancer: Insights for Program Development
Bibliographic record
Abstract
BACKGROUND: Healthy lifestyle behaviours may improve outcomes for people with colorectal cancer (crc), but the intention to take action and to change those behaviours may vary with time and resource availability. We aimed to estimate the prevalence of current lifestyle behaviours in people with and without crc in our community, and to identify their desire to change and their resource preferences. METHODS: A mixed-methods survey was completed by people diagnosed with crc who were pre-treatment (n = 54), undergoing treatment (n = 62), or done with treatment for less than 6 months (n = 67) or for more than 6 months (n = 178), and by people without cancer (n = 83). RESULTS: Current lifestyle behaviours were similar in all groups, with the exception of vigorous physical activity levels, which were significantly lower in the pre-treatment and ongoing treatment respondents than in cancer-free respondents. Significantly more crc respondents than respondents without cancer had made lifestyle changes. Among the crc respondents, dietary change was the change most frequently made (39.3%), and increased physical activity was the change most frequently desired (39.1%). Respondents wanted to use complementary and alternative medicine (cam), reading materials, self-efficacy, and group activities to make future changes. CONCLUSIONS: Resources for lifestyle change should be made available for people diagnosed with crc, and should be tailored to address physical activity, cam, and diet. Lifestyle programs offered throughout the cancer trajectory and beyond treatment completion might be well received by people with crc.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".