Perception of modern radiotherapy learning: study protocol for a mixed-methods analysis of trainees and trainers at a UK cancer centre
Bibliographic record
Abstract
INTRODUCTION: Radiotherapy technology and postgraduate medical training have both evolved significantly over the last 20 years. Clinical Oncology is a recognised craft specialty where the apprenticeship model of clinical training is applicable. The challenges of learning radiotherapy in the modern radiotherapy department workplace have not been comprehensively described and no optimal method has been identified. METHODS AND ANALYSIS: Five Clinical Oncology trainers and five Clinical Oncology trainees at a regional cancer centre will be invited to undertake a semistructured interview regarding their personal accounts of learning radiotherapy. Both trainees and consultants will be treated as equal co-investors in the process of radiotherapy learning, with the common shared aim of passing radiotherapy skills from trainers to trainees. Interviews will last up to 40 min. After transcription, an interpretative phenomenological analysis will be performed. All trainees and trainers at the same centre (n=34) will then be invited to complete the same purpose-built questionnaire. Four trainers and three trainees have piloted the questionnaire, and input was sought from the national leads of the biennial UK Clinical Oncology training survey. Significance testing will be performed on predefined questions and thematic analysis on white space questions. ETHICS AND DISSEMINATION: Medical education research is evolving in Clinical Oncology and Radiation Oncology but there are few studies comprehensively assessing this from the viewpoint of trainees and trainers. Pending the success of the proposed study, the approach detailed represents a novel method that could be used to identify the strengths and weaknesses of radiotherapy training in other centres and settings. Ethical and governance approvals have been granted by the University Research Ethics Committee and the Integrated Research Application System, respectively. This study has been funded by Friends of the Cancer Centre.
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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 | 0.093 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".