Humanism in global oncology curricula: An emerging priority.
Bibliographic record
Abstract
10505 Background: Training in humanism provides the skills to achieve shared decision making with patients and their families, to navigate systems level challenges and to function positively within the healthcare team. However, there is potentially a lack of attention to humanistic competencies in global oncology curricula due to the dominance of the biomedical model in curriculum design, the challenge of assessing humanistic competencies and global cultural considerations. The aims of this study were to explore to what extent humanistic competencies are included in global oncology curricula and the nature of the humanistic competencies included. Methods: Sixteen global oncology curricula identified in a prior systematic review were analysed. The curricula were coded using the Gold Foundation’s I.E.C.A.R.E.S (Integrity, Excellence, Collaboration & compassion, Altruism, Respect & Resilience, Empathy and Service) humanistic competency framework and the CanMEDS framework. Descriptive statistics were used to describe the proportion of items attributed to each aspect of the framework. Results: 7733 curricular items were identified in the 16 curricula and 729 (9%) aligned with the I.E.C.A.R.E.S framework. The proportion of humanistic items in individual curricula ranged from 2% to 26%. The proportion of humanistic items has been increasing from the curricula published in 1980-1989 (3%) to the curricula published in 2010-2017 with a mean of 11% (4 to 25%). There was a higher proportion of humanistic competencies in curricula from the European region (9%) than in other regions. Of the humanistic items 35% were under respect, 31% under compassion, 24% under empathy, 5% were under integrity, 2% under excellence, 1% under altruism, and 1% under service. The majority of the humanistic items also aligned with the professional (35%), medical expert (31%) or communicator (26%) CanMEDS domains. Conclusions: The proportion of humanistic competencies has been increasing in global oncology curricula over time however the overall proportion remains low. Humanism is largely represented by competencies of respect, compassion and empathy and there exists a conflation between humanism and professionalism. Future global curricular efforts may benefit from attention to incorporating all aspects of humanistic competencies.
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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 | 0.035 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".