Virtual Teaching and Training Models in Pediatric Oncology: A Retrospective Study from an LMIC
Notice bibliographique
Résumé
Abstract Introduction A multidisciplinary approach is essential for success in pediatric oncology treatment. Updated protocols, quality nursing care, psychosocial support, safe and standardized preparation of chemotherapy, infection control, and effective data management are key shareholders for the effective management of childhood cancer. The Department of Pediatric Oncology at Indus Hospital and Health Network (IHHN) initiated consistent teaching and trainings with the help of the My Child Matters Grant from Sanofi Espoir Foundation. These courses were conducted in person starting in 2019 and had to be postponed and restructured due to coronavirus (COVID-19) pandemic in early 2020. Objectives The aim of this study was to determine the impact of virtual teaching models for healthcare workers employed in pediatric hematology/oncology departments in low-resource settings. Materials and Methods After in-person courses in 2019, courses for all six disciplines (physicians, nursing, infection control, pharmacy, psychosocial care, and cancer registry) were conducted virtually starting December 2020, open to all and free of cost. A total of 878 registrations were obtained and 267 certifications given. Lectures with Q&A sessions were conducted via zoom and recordings shared through email. Each course was conducted by the relevant department at IHHN with pre- and postassessment conducted through Google Forms. Session feedback was taken through zoom polls and a comprehensive course feedback conducted after completion; e-certificates were awarded to successful participants according to a predetermined criterion. Results A total of 434 physicians' registrations were done from around Pakistan and countries like Saudi Arabia, Malaysia, Jordan, and Canada for the online physicians' course, of which 110 received certifications after completing post-test and attendance criteria of 55%. Pharmacy, infection control, psychosocial care, and cancer registry courses saw participation and certification of 51, 41, 24, and 14 participants, respectively. Online sessions received positive feedback in terms of instructors, course content, convenience, and access from over 90% participants. Conclusion Due to the ease in coordinating hectic schedules and cost-effectiveness of online lectures, this virtual teaching model will persist despite the trajectory of the COVID-19 pandemic. Similar ventures aimed at pediatric oncology teaching and training are needed in a widespread manner to improve outcomes of childhood cancer.
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 | 0,009 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,004 |
| 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 ».