The impact of a well breast cancer patient transition model of care on oncologists’ practice.
Notice bibliographique
Résumé
39 Background: The transitioning of well breast cancer patients, post-treatment, to family physicians is accepted as a safe and effective model of care. Numerous studies have focused on patients’ experience and health outcomes, but research has not examined the experience of oncologists who are practicing in this model of care. The purpose of this research is to explore the impact of a transition model of care on oncologists’ practice. Methods: Purposive sampling was employed to recruit and interview oncologists who have been transitioning patients to family physicians for two or more years. A total of 15 medical and radiation oncologists practicing in Ontario, Canada were interviewed. Data were analyzed using thematic analysis. Results: Most oncologists interviewed were confident that the transitioning of patients to family physicians is safe for many patients post-treatment. Despite some concerns about the feasibility of the model, namely acceptance of the model among patients and family physicians, oncologists perceived that the model enhances efficiency and sustainability of the cancer system. As the volume of patients in follow-up decreased, oncologists saw a variety of impacts including: a reduction in overbooking in their clinics; more new patients able to be seen in consultation each week; more flexibility in their schedules to accept urgent appointments; and an increased ability to spend more time with patients who are dealing with complex issues. For some oncologists, well patient appointments are a rewarding part of their work. Meanwhile, many experience challenges with increased intensity of workload. Oncologists recognize that some patients and family physicians may be reluctant but they believe that early communication with patients about eventual transition, and improved communication with family physicians can enhance the acceptability of this model of care. Conclusions: Oncologists interviewed in this study reported that the time they used to spend with well follow-up patients can now be spent on other activities that contribute to the enhancement of quality of care for cancer survivors and efficiency in the broader cancer system.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».