Importance of Incorporating the Perspectives of People with Cancer into Oncology Education: A Scoping Review
Bibliographic record
Abstract
Background: With the shift towards person-centered care (PCC) in oncology, there is a need for parallel evolution of oncology education programs to prepare the next generation of health professionals to deliver effective PCC. These programs should be designed utilizing perspectives from individuals who have lived experience with cancer to ensure that changes to education curricula translate to improved PCC in the clinic. Objectives: Our goal was to identify existing literature describing such programs as well as identify gaps for further development. Methods: Keywords were agreed upon and searched across Ovid Medline, Ovid Embase, ERIC, Google Scholar, and MedEdPORTAL databases. Duplicates were removed, unique articles were screened for relevance by title and abstract, and a full text review of each article was completed for validation. Included articles describe methods for involving people with cancer in developing and/or delivering oncology-focused education programs. Results: In total, 15 articles describing 12 unique oncology education programs from 7 different countries were identified, reviewed, and summarized. These programs involved learners undertaking undergraduate medical education, postgraduate medical education, continuing medical education, or training as nurses or radiation therapists. Current literature indicates that classroom-based sessions, experiential or simulated learning modalities, and/or asynchronous online modules can all feasibly be created with the integration of perspectives/narratives of people with cancer. Furthermore, involving people with cancer directly in the design and/or delivery of these education programs may contribute to improved patient experiences. Conclusions: Including the perspectives of people with cancer directly in oncology curriculum development and delivery can improve established pedagogical approaches and enhance learner confidence and competency in delivering PCC. We provide recommendations for stepwise implementation of patient perspectives into oncology education, with the hope that future programs will better prepare and motivate learners to provide PCC aimed at improving cancer care, quality of life, and disease outcomes.
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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 | 0.027 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.020 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".