Feasibility of implementing a virtual nursing-led smoking cessation clinic for patients with cancer.
Bibliographic record
Abstract
527 Background: Despite the importance of smoking cessation in cancer care, patients often find it challenging to quit smoking. Many patients interested in quitting may not be provided with adequate support or opportunities to be connected to smoking cessation resources. Given the advances in virtual care, we piloted a virtual nursing-led smoking cessation clinic to follow up with cancer survivors who were identified using tobacco. Methods: A virtual nursing-led clinic was piloted from November 2023 to May 2024 at the Princess Margaret Cancer Centre (Toronto, Canada). Using EPIC, monthly reports identified new patients diagnosed with cancer who reported smoking within 6 months of their initial visit but were not offered or declined smoking cessation support. These patients were called weekly up to four times by a trained oncology nurse until successfully contacted. Descriptive statistics were used to characterize the feasibility outcomes. Results: Among 191 patients eligible for contact, the median age was 64 years old (range: 26 to 94 years) and 63% were male. The most common disease sites were head and neck (16%), gastrointestinal (15%), hematological (15%), and lung (10%). A total of 339 calls were conducted and an average of 1.4 calls (range: 1 to 4) were needed to successfully reach a patient. Twenty-two clinics were conducted, where an average of 22 calls were made per clinic. Most patients (73%) were reached after one call, while 13% required two calls, and 15% required three or more calls. Among the patients reached (n = 196), the average duration per call was 3.6 minutes (range: 1 to 15 minutes). Among patients contacted, 14% accepted a referral for smoking cessation support, while among those who declined, 36% had already quit prior to receiving a phone call. For patients accepting a referral, call durations were slightly longer (5.5 minutes vs. 2.7 minutes, p < 0.005). Common patient-reported barriers to accepting a referral included a lack of readiness to quit, stress and exposure to second-hand smoke. Conclusions: A virtual nurse-led smoking cessation clinic is feasible to help provide referrals to supporting smoking cessation among patients with cancer who initially declined support or have never been offered support. Many patients declining referral to support had already quit smoking, while other barriers include stress, exposure to second-hand smoke, and readiness to quit. Strategies to minimize and reduce these barriers should be further explored.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".