A Canadian national needs assessment for an immunology curriculum for hematology residents
Bibliographic record
Abstract
Abstract Background: Hematology training in Canada is competency-based and consists of two years of integrated clinical and laboratory training following completion of internal medicine residency. Understanding the fundamental principles of immunology is critical to both clinical practice and scholarship in hematology. However, there is currently no national consensus on which immunology topics should be taught in Canadian hematology training programs. We conducted a national needs assessment of immunology teaching topics, as perceived by hematologists and trainees, as an initial step in Kern's model for curriculum development. Methods: An online REDCap survey was distributed across Canada to current hematology residents (postgraduate years 4 and 5), recent (within two years) graduates, current and former (within five years) hematology program directors (PDs), academic hematologists with clinical teaching roles, and community hematologists with leadership roles and/or experience delivering immunotherapies in community settings (purposive sampling). Results: There were 88 survey respondents. Fifty-five identified as female, 32 as male, and one preferred not to say. 27% were current trainees, 25% recent graduates, 25% academic (non-PD) hematologists, 15% current or former PDs, and 8% were community hematologists. Forty-seven (53%) resided in Ontario, 14 (16%) in Quebec, 12 (14%) in British Columbia, 10 (11%) in Alberta, 2 (2%) each in Manitoba and Nova Scotia, and 1 (1%) in Newfoundland. Of the non-trainees, 27 (42%) had been in independent practice for ≤2 years; 11 (17%) for >2 to ≤5 years; 4 (6%) for >5 to ≤10 years; 8 (13%) for >10 to ≤15 years; 7 (11%) for >15 to ≤20 years, and 4 (6%) for >20 years. The percentage of non-trainees who spend ≥50% of clinical time in malignant hematology, classical hematology, transfusion medicine, stem cell transplantation or other was 56%, 27%, 11%, 8% and 3%, respectively. Sixty-four percent of respondents self-rated their knowledge of immunology concepts as “basic” (“I know the fundamentals and could explain these concepts to patients”), 25% as “limited” (“I’ve been introduced to some of these concepts”), 8% as “expert” and 2% as “no knowledge.” Nine of the 13 (69%) current and former PDs reported that their programs included dedicated teaching on immunology topics; only 33% of non-PDs (trainees and hematologists) indicated this and 15% could not recall. According to PDs, the topics most commonly taught included mechanisms of immunosuppressive therapies and immune therapeutics, B cell development and activation, immune checkpoints, the function of cells/tissues of the immune system, and HLA biology. Respondents were asked whether the current amount of immunology taught in their programs was adequate to prepare residents for clinical hematology practice. On a sliding scale from 0 (far too little content) to 5 (just right) to 10 (far too much content), the median score was 3, and 70% of respondents selected either 2 or 3. This was similar across PDs and non-PDs. Seventy-eight percent of non-trainees indicated that immunology concepts were either “extremely” or “very” relevant to everyday clinical hematology practice; 17%, 2% and 2% indicated they were occasionally, rarely and not relevant, respectively. Eighty percent of respondents felt it was either “very” or “extremely” important to incorporate immunology topics into hematology training; 17%, 2% and 0% felt it was either “somewhat important”, “neutral”, or “not important”, respectively. Greater immunology knowledge was felt to have the greatest potential impact on respondents' comfort with the following clinical activities: prescribing treatments that harness the immune system, counselling patients starting immunotherapies, managing patients with acquired immunodeficiency, and interpreting/critically appraising scientific literature involving immunotherapies or disease biology. Respondents' preferred formats of curricular delivery were an immunology bootcamp (77%), online learning modules (60%) and case-based teaching (61%). Conclusion: There is broad agreement among trainees, PDs, and practicing hematologists across Canada that current immunology teaching in hematology training programs is insufficient and a dedicated immunology curriculum would enhance competencies in clinical hematology practice. The next step involves a Delphi process to achieve consensus on key curricular topics to be incorporated into a future national curriculum.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".