Feasibility of implementing a virtual nursing-led smoking cessation clinic for patients with cancer.
Notice bibliographique
Résumé
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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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 | 0,007 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,001 |
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 ».