Narrative Expertise in Oncology: An Integrated Training Model to Advance the Field
Bibliographic record
Abstract
Unlabelled: Despite growing evidence that narrative expertise may benefit cancer care professionals and the field, few hematology-oncology trainees pursue graduate degrees in the humanities. For those trainees with a particular interest in humanism in medicine, we advocate for integration of a Master of Fine Arts (MFA) degree concurrent with fellowship training. This pathway enables trainees to gain advanced skills in narrative competence, informing research and scholarly activities during fellowship and building a foundation for future careers that promote humanism in the field of hematology-oncology across clinical practice, education, research, and advocacy. Narrative competence describes the ability to create space for and elevate the voices of patients, families, and clinicians, which includes active listening, reflecting, sharing, and being moved by stories. In this paper, we review evidence suggesting that frequent exposure to suffering can threaten career longevity for cancer care clinicians, and we highlight narrative competence as an approach to mitigate moral distress, improve well-being, and bolster resilience for our workforce. The influence of narrative competence extends beyond patient care, with meaningful ramifications for advancing research, education, and advocacy efforts across the field. We encourage institutions with hematology-oncology fellowship programs that have capacity to support graduate studies to include the MFA as an option for trainees who aim to become thought leaders and experts in narrative competence. The MFA serves as a strategic mechanism to invest in growing the next generation of hematologist-oncologists with expertise in narrative competence to advance the field.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".