Development of a National Needs Driven Intrinsic Roles Curriculum for Canadian Hematology Residency Training Programs
Bibliographic record
Abstract
Introduction: CanMEDS is a widely used postgraduate medical education framework detailing seven roles necessary for physician competence. The “intrinsic” CanMEDS roles-Professional, Communicator, Collaborator, Leader, Health Advocate and Scholar-center around the Medical Expert role and are largely taught through the hidden curriculum by role modeling and observation. Despite the advent of competency based medical education (CBME), these roles are often underemphasized in the visible curriculum, with up to 40% of intrinsic role competencies not mapped to evaluations or absent from existing curricula. Additionally, Canadian CBME consists of four training phases, with the highest representation of intrinsic roles teaching occurring during Transition To Practice, which is the shortest phase occurring at the very end of training. We sought to explore the perceived needs for intrinsic roles (IR) teaching for Canadian hematology residency training programs, with a goal of implementing a national curriculum in residency training. Methods: An environmental scan of IR curricula within Canada and a targeted needs assessment of the hematology residents and program director (PD) at McMaster University (convenience sampling) were conducted in early 2022. Responses were mapped to existing curriculum milestones within the CanMEDs framework. This informed the delivery of a pilot IR workshop (virtual, May 2022) during the McMaster University hematology academic half day. Residents engaged in a group debrief immediately following the workshop and were subsequently asked to complete individual retrospective pre-post self-assessments evaluating their competency within each addressed IR on a 5 point Likert scale. We then conducted a national cross-sectional survey of hematology PDs (Spring 2023) to explore existing curricula, gauge interest in a national curriculum, and understand local needs. Hamilton Integrated Research Ethics Board approval was obtained to conduct the national survey. Data were analyzed descriptively. Results: The environmental scan identified several pre-existing tools for IR teaching and evaluation, many developed by the Royal College (Canadian licensing body), with limited utilization within hematology training programs in 2022. All four McMaster Hematology residents completed the needs assessment identifying numerous milestones within the Leader, Collaborator and Scholar roles, with minimal emphasis on the Communicator and Health Advocate roles. The McMaster University PD identified similar themes as the residents, with further emphasis on the Professional role, highlighting the need to “Recognize and manage conflicts of interest” and “Demonstrate a commitment to physician health and well-being to foster optimal patient care.” Our pilot workshop, attended by five residents, underscored the demand for a longitudinal, stage-adapted curriculum that includes interactive and in-person elements, along with pre-session materials to allow for self-reflection. A notable preference for limiting topics to 1-2 per session and focusing on learner-driven interests was also highlighted. The workshop self-assessment tool showed self-reported improvement in the Leader and Scholar role competencies, which aligns with time devoted to each IR during the session. The national survey of PDs, with 50% (8/16) response rate, revealed that the Scholar role dominates existing curricula, with lesser emphasis on other IR. PDs underscored the value of integrating teaching into daily clinical practice and expressed interest in a national curriculum, particularly emphasizing the Health Advocate role. Concerns about additional time demands on faculty and trainees to deliver and attend the new curriculum, as well as the need for appropriate evaluation measures, were noted. The need for faculty development and continuous quality improvement based on resident feedback was also emphasized. Conclusions: Hematology trainees and PDs acknowledged the importance of an early and longitudinal curriculum exploring the IR within Hematology residency, although there was disagreement about exactly which topics should be included. Local residents valued an interactive workshop dedicated to the intrinsic roles, which may be scalable into a national curriculum. Potential next steps include further development of this curriculum at a national hematology residents retreat.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".