Physician-brief advice for promoting smoking cessation among cancer patients on treatment in low and middle-income countries: a scoping review
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,006 | 0,033 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,004 | 0,005 |
| Bibliométrie | 0,007 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,003 | 0,002 |
| Science ouverte | 0,002 | 0,001 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».