Competency-Based Workforce Development and Education in Global Oncology
Bibliographic record
Abstract
The healthcare workforce plays a pivotal role in cancer care delivery, leadership, policy, education, and research in complex cancer systems. To ensure quality and relevance, health professionals must have the necessary competencies to deliver patient-centered and efficient care, coupled with the ability to work in teams and manage health resources wisely. This paper aims to review the concept of competency-based medical education (CBME) in the context of oncology to provide insights and guidance for those interested in adopting or adapting competency-based education in training programs. The results of a scoping review of CBME in oncology are presented here to describe the current status of CBME in oncology. The literature describing the implementation and evaluation of CBME in oncology training programs for medical professionals internationally is summarized and key themes identified to provide practical guidance for educators. Further, the paper identifies critical competencies for oncology education and training globally and presents recommendations and opportunities for collaboration in competency-based education and training in oncology. The authors argue for increased global collaboration and networking in the realm of CBME to facilitate the establishment of a competent global cancer care workforce.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".