Humanism in Global Oncology Curricula: An Emerging Priority
Bibliographic record
Abstract
Introduction: Training in humanism provides skills important for improving the quality of care received by patients, achieving shared decision-making with patients, and navigating systems-level challenges. However, because of the dominance of the biomedical model, there is potentially a lack of attention to humanistic competencies in global oncology curricula. In the present study, we aimed to explore the incorporation of humanistic competencies into global oncology curricula. Methods: This analysis considered 17 global oncology curricula. A curricular item was coded as either humanistic (as defined by the iecares framework) or non-humanistic. If identified as humanistic, the item was coded using an aspect of humanism, such as Altruism, from the iecares framework. All items, humanistic and not, were coded under the canmeds framework using 1 of the 7 canmeds competency domains: Medical Expert, Communicator, Collaborator, Leader, Scholar, Professional, or Health Advocate. Results: Of 7792 identified curricular items in 17 curricula, 780 (10%) aligned with the iecares humanism framework. The proportion of humanistic items in individual curricula ranged from 2% to 26%, and the proportion increased from 3% in the oldest curricula to 11% in the most recent curricula. Of the humanistic items, 35% were coded under Respect, 31% under Compassion, 24% under Empathy, 5% under Integrity, 2% under Excellence, 1% under Altruism, and 1% under Service. Within the canmeds domains, the humanistic items aligned mostly with Professional (35%), Medical Expert (31%), or Communicator (25%). Conclusions: The proportion of humanistic competencies has been increasing in global oncology curricula over time, but the overall proportion remains low and represents a largely Western perspective on what constitutes humanism in health care. The representation of humanism focuses primarily on the iecares attributes of Respect, Compassion, and Empathy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".