Learning radiotherapy: the state of the art
Bibliographic record
Abstract
BACKGROUND: The last two decades have seen revolutionary developments in both radiotherapy technology and postgraduate medical training. Trainees are expected to attain competencies using a mix of experiential learning, formal postgraduate teaching, self-directed learning and peer education. Radiation (Clinical) Oncology is a recognised 'craft specialty' where the apprenticeship model of training is applicable. This scoping review examines the evidence in relation to how medical trainees learn radiotherapy. METHODS: A systematic search of MEDINE and EMBASE was undertaken to identify studies of trainee and/or trainer experience of radiotherapy learning published 1999-2018. Results pertaining to Medical Oncology, workforce trends, undergraduate radiotherapy exposure, academic training, global health, non-medical staff, health service infrastructure and recruitment to training programmes were not included. RESULTS: A total of 146 publications were included in the synthesis. Five themes were apparent through careful iterative analysis representing broadly inter-related issues. Most articles studied radiotherapy training from the perspective of the trainee doctor. Most literature reports results of observational, local or national surveys with a tightly defined scope. Considerable variation exists within hospitals, within countries, over time and between different curricular areas. CONCLUSIONS: Medical education has not kept pace with changes in the field of radiotherapy and large differences are demonstrated in experience between trainees in different hospitals, countries and training stages. Interpersonal relationships, departmental organisation, and national curricula impact on training quality. Qualitative and quantitative research examining modern radiotherapy learning has been uncommon and uncoordinated, until recently. To date no single study has been designed to comprehensively assess a department's training scheme.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".