“I Really Wish We Had a Talk on Advocacy That Wasn't Boring”: Toward a Health Advocacy Curriculum That Meets the Needs of Canadian Hematology Residents
Bibliographic record
Abstract
Background: There is an ongoing need to incorporate health advocacy training into postgraduate medical education. In Canadian hematology training, there are no existing dedicated health advocacy curricula. As a first step toward the development of such curricula, we sought to determine what the health advocate role entails for hematologists, identify necessary health advocacy competencies in hematology, and understand how these competencies could be learned in hematology residency training. Methods: Semi-structured interviews were conducted with practicing hematologists in Ontario, Canada to elicit key health advocacy competencies for hematologists and learn how these are acquired. Participants were selected using a purposeful, maximal variation sampling strategy. Interviews were recorded in their entirety and transcribed by hand. Data were deidentified after collection and a computer software (NVivo) was used to create codes through an iterative approach. The codes were analyzed using the thematic analysis technique outline by Braun and Clarke (Braun & Clarke, 2006). A deductive approach was employed, incorporating the CanMEDs framework and previously described conceptual frameworks pertaining to health advocacy. Results: 8 hematologists (5 academic-based, 3 community-based; 4 malignant hematologists and 4 non-malignant hematologists) were included. Hematologists engage in advocacy activities at the clinical, paraclinical, and supraclinical levels and conduct advocacy work most commonly through a directed agency strategy. Drug access was the most prominent advocacy challenge encountered by hematologists. Health disparities that were identified included the rural/remote divide and socioeconomic status. Health advocacy competencies employed by hematologists closely resembled those used by other physicians, with some difference on emphasis. Skills that hematologists found were important to advocacy training were leveraging interprofessional connections, using research as advocacy, and acquiring health systems knowledge. Early exposure to advocacy in training, effective mentorship, and learning from practical examples and experience were viewed as the most effective ways to learn health advocacy skills. Conclusion: This study identified the breadth of advocacy activities conducted by hematologists and identified numerous competencies that are important to being an effective health advocate. Health advocacy education in hematology training programs should start early, be practical and engaging, be integrated longitudinally into existing teaching and learning activities, and harness the power of mentorship relationships. This work is the first step in addressing the informational gap required to design a novel health advocacy curriculum for hematology residents.
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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 | 0.011 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.027 | 0.010 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".