Synthesis of Existent Oncology Curricula for Primary Care Providers: A Scoping Review With a Global Equity Lens
Bibliographic record
Abstract
PURPOSE: Global increases in cancer, coupled with a shortage of cancer specialists, has led to an increasing role for primary care providers (PCP) in cancer care. This review aimed to examine all extant cancer curricula for PCPs and to analyze the motivations for curriculum development. METHODS: A comprehensive literature search was conducted from inception to October 13, 2021, with no language restrictions. The initial search yielded 11,162 articles and 10,902 articles underwent title and abstract review. After full-text review, 139 articles were included. Numeric and thematic analyses were conducted and education programs were evaluated using Bloom's taxonomy. RESULTS: Most curricula were developed in high-income countries (HICs), with 58% in the United States. Cancer-specific curricula focused on HIC priority cancers, such as skin/melanoma, and did not represent the global cancer burden. Most (80%) curricula were developed for staff physicians and 73% focused on cancer screening. More than half (57%) of programs were delivered in person, with a shift toward online delivery over time. Less than half (46%) of programs were codeveloped with PCPs and 34% did not involve PCPs in the program design and development. Curricula were primarily developed to improve cancer knowledge, and 72 studies assessed multiple outcome measures. No studies included the top two levels of Bloom's taxonomy of learning (evaluating; creating). CONCLUSION: To our knowledge, this is the first review to assess the current state of cancer curricula for PCPs with a global focus. This review shows that extant curricula are primarily developed in HICs, do not represent the global cancer burden, and focus on cancer screening. This review lays a foundation to advance the cocreation of curricula that are aligned to the global cancer burden.
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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 | 0.031 | 0.118 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.021 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".