Cancer patient interest and perceptions of health behavior change programs.
Bibliographic record
Abstract
98 Background: Health behavior change including smoking cessation, physical activity (PA) and alcohol moderation are important aspects of cancer survivorship. We assessed cancer pt interest and perceptions of programs for these behaviours. Methods: 501 cancer pts were surveyed on their smoking, PA and alcohol use along with their interest and perceptions for programs for these behaviors. Multivariate logistic regression models identified factors associated with pt interest and perceptions. Results: At diagnosis, 115 pts smoked; 184 were exposed to second hand smoke (SHS); 313 did not meet PA guidelines; 238 were drinking alcohol. At risk pts’ (e.g, smokers for smoking cessation, SHS exposed for household smoking cessation) survey results are shown in the table. Most pts perceived smoking (90%), SHS (83%) and alcohol (56%) to be harmful on quality of life, survival and fatigue while PA (77%) was felt to improve these outcomes. These perceptions were not associated with program interest ( P> 0.05). However, pts perceiving that alcohol worsened and PA improved these outcomes were more to likely believe associated programs are beneficial (alcohol aORs = 2.1-2.2 P< 0.03; PA aORs = 1.9-3.2 P< 0.02) and should be routine care (alcohol aORs = 1.9-3.5 P< 0.03; PA aORs = 1.7-2.4 P< 0.1). Pts with more pack-yrs smoked less likely perceived a benefit in a household smoking cessation program (aOR = 1.02 P< 0.007). Pts preferred discussing programs with doctors ( > 35%) or counsellors ( > 42%). Conclusions: About half of pts feel that health behavior change programs would be beneficial and should be part of routine care. These factors were more important than perception of the behaviors on outcomes in influencing pt interest. Initial discussions with pts should focus on discussing benefits of these programs. [Table: see text]
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".