Need for Competency-Based Radiation Oncology Education in Developing Countries
Bibliographic record
Abstract
Although not a new concept in itself, competency-based education has set the trend for the globally accepted standard norm for education and training of medical professionals including postgraduate education in radiation oncology. Societal needs demand from radiation oncologists that they be not only competent in the knowledge and skills relevant to their specific discipline, but that they also display competencies such as professionalism, scholarship, health advocacy, management/leadership, collaboration and communication. The realities of developing countries, in particular low and middle income countries (LMICs) set different priorities than in high income countries. A large proportion of cancer patients do not have access to adequate radiotherapy services. Resource constraints determine limitations in equipment, accessories, and dosimetry. Lower than standard staffing levels and limited quality education and training also contribute to substandard care and clinical outcomes. In this environment, the addition and assessment of competency-based elements to training programmes can be challenging. On the other hand, it is precisely in these countries, where competencies such as the ones listed above are highly needed in the radiation oncology profession. Implementation of competency-based medical education in the education of radiation oncologists in LMICs is both a need and a challenge. The available frameworks and competencies, despite being very relevant to the realities faced by radiation oncologists in LMICs, will still need to be adapted in order to ensure effective implementation at the national/regional level. Radiation oncologists need to employ effective change-management strategies to ensure that the changes which are introduced can remain sustainable within the context of national healthcare, education and political systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".