Finding the Gaps: Perceived and Unperceived Needs in Malignant Hematology Training in Canadian Hematology Residents
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".