Can radiation oncologists learn to be better leaders? Outcomes of a pilot Foundations of Leadership in Radiation Oncology program for trainees delivered via personal electronic devices
Bibliographic record
Abstract
INTRODUCTION: There has been no systematic attempt to enhance leadership capacity within radiation oncology as an integrated component of training. This pilot study examines an intervention to introduce basics of leadership learning to radiation oncology trainees. METHODS: A case-based learning tool was designed for delivery via trainees' personal electronic devices. Eight typical workplace case scenarios representing leadership challenges were followed by multiple choice questions, key learning points and hyperlinks to relevant resources. Cases were automatically sent every few days over 4 weeks and participants' responses anonymously collated by the delivery platform (QStream). In addition, an online survey was sent at completion of the program to capture trainees' perspectives on the utility of this tool. RESULTS: Thirty-seven of 45 (82%) trainees participated: 21 females and 16 males. Twenty-six of 37 (70%) starting the program completed it. Sixteen (62% of 'completers') responded to the post-program survey. Fourteen of 16 (87.5%) agreed to the program and helped them identify ways they were already exhibiting leadership. Eleven of 16 (68.8%) agreed they had acquired knowledge that could assist them in being better leaders. Fifteen of 16 said the program made them consider future leadership possibilities in radiation oncology. Fourteen of 15 enjoyed the digital format. Most suggestions for improvement linked to a desire for more interactivity in learning these skills. CONCLUSION: Piloting an online tool designed to introduce foundation leadership concepts to radiation oncology trainees has provided useful feedback to guide further development in this area. Although this method had high feasibility, it revealed the need for additional interactive methods for leadership learning.
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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".