Cancer patients' interest and preferences for an inpatient smoking cessation program (SCP).
Bibliographic record
Abstract
10090 Background: Smoking cessation is becoming an integral part of cancer survivorship care. We assessed cancer patients’ interest and preferences in the design of a SCP. Methods: Cancer patients from all subtypes were cross-sectionally surveyed. Multivariable logistic regression analyses identified factors associated with patient interest and preferences. Thematic qualitative analyses supplemented survey data. Results: Of 985 cancer patients, 23% smoked in the year prior to diagnosis (“current smokers”), of whom 56% quit afterwards; 55% had tobacco related cancers. Among current smokers, 35% of patients were interested in an ambulatory SCP; while 51% were interested in an inpatient SCP (ISCP). Multivariable analysis revealed that higher income (aOR = 3.22 95%CI [1.47-7.14]) and receiving palliative treatment (aOR = 2.90 [1.14-7.38]) were associated with interest in an ISCP. Perceiving that smoking was harmful to quality of life, survival and fatigue were each associated with a greater belief that SCPs are beneficial (aORs = 3.39-4.75, P< 0.001). Believing that a SCP was beneficial to patients (51%) (aOR = 4.65 [2.15-10.03]) or that a SCP should be routine cancer care (64%) (aOR = 4.22 [1.90-9.39]) were each associated with preference for joining an ISCP. Major reasons for lack of interest in joining an ISCP include having just quit prior to diagnosis (26%) and wanting to quit alone (23%). Only 65% of patients interested in ISCPs wished to discuss it at their first oncology visit. Feeling overwhelmed (50%) and wanting to control discussion about smoking cessation (31%) were the major barriers to discussing at first visit. Interestingly, neither level of patient knowledge nor perceptions of smoking on outcomes were associated with interest in ISCP. Significantly fewer patients wanted phone (24%) or WebApp (15%) counselling. Conclusions: ISCPs were favored by cancer patients smoking at diagnosis. Believing in a benefit of a SCP was a more important factor in wanting to join an ISCP than knowledge and perception of the effects of smoking on cancer outcomes. Initial cancer care discussions with patients should highlight the effectiveness of SCPs. ISCPs should be explored as options for cancer patients being admitted for any reason.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".