Physician-brief advice for promoting smoking cessation among cancer patients on treatment in low and middle-income countries: a scoping review
Bibliographic record
Abstract
INTRODUCTION: Physician-brief advice has been utilized in high-income countries to promote smoking cessation among cancer patients. Empirical evidence on its effectiveness among cancer patients in low and middle-income countries (LMICs) is lacking. The gap could be due to inadequate training, and competing healthcare priorities, leading to insufficient implementation of targeted smoking cessation interventions in oncology settings. We undertook this scoping review to determine if physician-brief advice is effective in promoting smoking cessation among cancer patients in LMICs. METHODS: We conducted a literature search of all relevant articles across five databases: Cochrane Central Register of Controlled Trials, Cochrane Library (Tobacco Addiction Group trials), World Conference on Lung Cancer proceedings, PubMed, and Google Scholar up to November 2023, using pre-defined inclusion criteria and keywords. The study population was cancer survivors in LMICs, the intervention was smoking cessation advice by a physician in a clinic or oncology center during a consultation, and the outcome was the effect of smoking cessation programs in discontinuing smoking among cancer survivors in LMICs. RESULTS: Overall, out of every 10 cancer patients in LMICs, about seven were smokers, and one-half had received physician-brief advice for smoking cessation. Physician-brief advice was more likely to be delivered to patients with smoking-related cancer (Cohen's d = 0.396). This means that there is a noticeable difference between patients with smoking-related cancer compared to those with cancer unrelated to smoking. Smoking cessation failure was due to the inability to cope with the symptoms of withdrawal, missed smoking cessation clinic visits, mental health disorders, limited time and resources, and minimal patient-physician contact. CONCLUSION: There is very little literature on the frequency of use or the efficacy of physician-brief advice on smoking cessation in LMICs. The literature suggests that cancer patients in LMICs have low self-efficacy to quit smoking, and smoking cessation is rarely part of cancer care in LMICs. Physicians in LMICs should be trained to use motivational messages and good counseling techniques to improve smoking cessation among cancer patients. Policymakers should allocate the resources to implement physician-brief advice and design training programs for physicians focusing on physician-brief advice tailored to cancer patients.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".