The differential needs and expectations from general practitioners in oncology between high-income countries and low- and-middle-income countries: results from a survey of Canadian and Nepali oncologists
Notice bibliographique
Résumé
Background: To address the shortage of oncologists in the wake of the rapidly increasing global cancer burden, general practitioners of oncology (GPOs) have been added to cancer care teams worldwide. GPOs are family physicians with additional training in oncology and their roles differ by both country and region. In this study, we aimed to learn about the roles and expectations of GPOs from the perspective of oncologists in Canada and Nepal. Methods: A survey was designed and administered to Canadian and Nepali Oncologists between February and November 2022 using Research Electronic Data Capture, a secure web-based software platform hosted at Queen's University in Kingston, Ontario, Canada. Participants were recruited through personal networks/social media in Nepal and the survey was distributed through an email list provided by the Canadian Association of Medical Oncologists. Results: The survey received 48 responses from Canadian and 7 responses from Nepali oncologists. Canadian respondents indicated that in terms of educational content delivery, clinics with oncologists followed by didactic lectures by oncologists were thought to be the most effective, followed by a small group learning and online education. Nepali oncologists also indicated didactic lectures by oncologists and small group learning would be the most effective teaching techniques, followed by online education and clinics with oncologists. Critical knowledge domains and skills most relevant for GPO training identified by Canadian respondents were managing pain and other common symptoms of cancers, as well as treatment of common side effects, followed by goals of care discussion, post-treatment surveillance for recurrence, and the management of long-term complications from treatment. Respondents from Nepal, however, suggested an approach to diagnosis to patient with increased risk of cancer, and cancer staging were the most critical knowledge domains and skills. The majority of oncologists in both countries thought a training program of 6-12 months was optimal. Conclusion: We found many similarities in oncologist's opinions of GPOs between the two countries, however, there were also some notable differences such as the need to provide cancer screening services in Nepal. This highlights the need to tailor GPO training programs based on local context.
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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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,003 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».