Development of an Electronic Learning Module for CAR-T Education in Canadian Hematology Residents
Notice bibliographique
Résumé
Introduction: Chimeric antigen receptor T-cell (CAR-T) therapy is a novel treatment for multiple hematologic malignancies. There are currently six approved CAR-T products in Canada and this therapy is provided in a select number of academic centres. While this therapy has shown promising efficacy, it is associated with unique toxicities which requires prompt recognition and management. As CAR-T therapy access increases, there is a growing need to ensure that physicians overseeing this therapy have received appropriate education and training. Canadian hematology graduate trainees experience variable exposure to CAR-T therapy management, mainly dependent on experiential learning. This can lead to gaps in trainee education. Electronic learning modules (ELMs) are an educational intervention that provide an accessible, flexible and interactive learning experience supporting adult learning principles, which have the potential to address such potential gaps in training. We present our process of developing a needs-driven novel CAR-T therapy ELM for hematology trainees in Canada. Methods: In March 2023, an online environmental scan was performed to identify the need for CAR-T education programs tailored to Canadian hematology trainees. Next, to determine optimal ELM content and delivery, a needs assessment survey was distributed online to Canadian hematology trainees in June 2023. The survey contained 10 items and included multiple choice, free text, and Likert scale questions addressing the following domains: 1) pre-existing CAR-T education, 2) content knowledge needs, 3) ideal features and parameters of a CAR-T ELM, 4) preferred learning methods, and 5) preferred assessment methods. A draft ELM was developed based on these results and with input from CAR-T therapy content experts. We conducted semi-structured focus groups of recently graduated Canadian hematology trainees to pilot the ELMs' module content, length, visuals, and delivery. The focus groups were conducted between January and February 2024. The qualitative analysis of transcripts was iterative until thematic saturation was reached. The ELM was modified based on focus group feedback to produce the final ELM product. Results: An environmental scan identified no open-access CAR-T ELMs that address the Canadian practice environment. Additionally, there were no online learning programs directed at hematology trainees. Sixty participants were approached and 17 completed the needs assessment, with representation from 72% of adult hematology residency programs in Canada. Most residents (71%) had some teaching around CAR-T, even if their residency training was not based at a CAR-T centre. 65% of residents had assessed a patient for CAR-T therapy eligibility, and 71% of residents had experience managing acute toxicities of CAR-T therapy. However, only 35% had experience assessing patients for potential bridging therapy prior to CAR-T, and less than 30% had provided long-term follow-up care following CAR-T therapy. Participants felt they would at least moderately benefit from learning more about all aspects of CAR-T therapy, and significantly benefit from learning more about patient selection, bridging, and acute and long-term toxicities. The preferred ELM length was 1-2 hours. To assess knowledge acquisition, participants strongly preferred a written quiz format over OSCE-style assessment. Eight participants took part in three focus groups to provide feedback on the ELM. Major themes of positive feedback included: 1) overall visual design, 2) user-friendliness of interface, 3) module content and length, and 4) availability and relevance of practice questions. Points of constructive feedback included adding in-line references and reference summaries, highlighting key information to draw attention, and providing proof of module completion for educational credit. Conclusion: A mixed methods research design was successfully employed to develop an electronic learning module for CAR-T education in Canadian hematology residents. The next step, which is currently in progress, is to evaluate the efficacy of this educational intervention across multiple levels using Kirkpatrick's training evaluation model. Future goals include disseminating the ELM through the Cellular Therapy and Transplant Canada website.
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 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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,017 | 0,003 |
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 ».