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Enregistrement W2980717214 · doi:10.1182/blood-2018-99-119738

Finding the Gaps: Perceived and Unperceived Needs in Malignant Hematology Training in Canadian Hematology Residents

2018· article· en· W2980717214 sur OpenAlexaffabout
Wilson Lam, Arjun Law, Umberin Najeeb, Danny Panisko, Raymond Jang, Hassan Sibai

Notice bibliographique

RevueBlood · 2018
Typearticle
Langueen
DomaineMedicine
ThématiqueLymphoma Diagnosis and Treatment
Établissements canadiensUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Organismes subventionnairesnon disponible
Mots-clésHematologyMedicineInternal medicineCurriculumCertificationFamily medicineMedical educationOncologyPsychologyPedagogy

Résumé

récupéré en direct d'OpenAlex

Abstract Background Malignant Hematology is in a new era of exciting novel treatment regimens and modalities, including CAR-T (Chimeric Antigen Receptor T-cells) and BiTE (Bi-specific T-cell Engaging) antibodies. Current trainees require an ever-increasing knowledge and skillset to deliver high quality care to more complex patients. There is limited evidence on the educational needs of hematology residents with these emerging complexities. Moreover, these educational needs themselves are poorly-defined. As a first step, we sought to perform a detailed needs assessment to identify knowledge gaps in our learners. It is our intention to use this information to aid in developing a curriculum incorporating these novel elements. Methods Every year Hematology residents in Canada (Post Graduate Year [PGY] 4 and above) attend the National Hematology Retreat in Toronto, Ontario for a weekend of educational activities, which also serves as preparation for the Royal College of Physicians and Surgeons Hematology certification exam. This past year, residents were invited to participate in a questionnaire to identify perceived and unperceived needs. They were asked to select topics of perceived needs from a pre-selected list. This was followed by a knowledge assessment using case-based questions in leukemia, myeloma, lymphoma, and Blood and Marrow Transplantation [BMT]. The study is approved by the University of Toronto Research Ethics Board. Data were analyzed descriptively as needed. Mean total scores from the case-based questions were compared between post-graduate years using one way ANOVA. All statistical calculations were performed using SPSS version 24. Results 35 of 70 Canadian Hematology residents attending the retreat responded to our survey. Among the respondents, seven were PGY-4, nine were PGY-5, and 19 were PGY-6. Of our pre-selected topics list, residents perceived the most common knowledge gaps existed in management of BMT complications, followed by molecular testing (especially genomics), and novel immune and cellular therapies. The top choices differed in the PGY-4 year (BMT complications, novel immune and cellular therapies and emergency AML complications, Figure 1). Among the respondents answering case-based questions, there was a significant difference in mean scores with increasing length of training (PGY-4: 53%, PGY-5: 70%, PGY-6: 79%, p=0.009). There was a knowledge gap in BMT among all levels of residents, which correlated with their perceived knowledge gaps. However, a majority of them correctly answered the questions on molecular testing and novel immune and cellular therapies. Conclusions Needs assessments are useful in assessing background knowledge and identifying perceived and unperceived needs of trainees. These can be used towards creating a resource that accounts for learning priorities. Our needs assessment of hematology residents across Canada demonstrated that:Knowledge gaps exist among residents at different levels of training, particularly in BMT, compared to other areas of Malignant Hematology. Moreover, this was perceived by residents themselves.Learning priorities of residents may change over the course of their training.Educational curricula should incorporate recent advances in hematology (molecular testing and novel immune and cellular therapies); however more emphasis should also be placed on BMT in general. Disclosures No relevant conflicts of interest to declare.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,016
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,978
Score d'incertitude au seuil0,364

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,016
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0050,002
Communication savante0,0020,001
Science ouverte0,0010,002
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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.

Tête enseignante Opus0,026
Tête enseignante GPT0,278
Écart entre enseignants0,252 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2018
Routes d'admission2
Résumé présentoui

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