A new wave of leaders: Early evaluation of the interdisciplinary Foundations of Leadership in Radiation Oncology (FLiRO) program
Bibliographic record
Abstract
Purpose: Effective leadership across all areas of radiation oncology (RO) is vital to fully realise the benefits of radiation therapy in cancer care. We report outcomes of a novel interdisciplinary leadership program designed for RO professionals under a global joint society initiative. Methods: The Foundations of Leadership in RO (FLiRO) program was designed for aspiring RO leaders. Initially delivered in a blended learning format, it was adapted to fully virtual in 2021. It comprised a webinar tutorial, on-line modules and homework followed by 'live' in-person/virtual workshops over an approximately 6-week period. Topics included personal awareness, effective teamwork, quality improvement skills, leading change and conflict management. An immediate post-program online survey was performed using Likert scales to measure self-reported educational value, interaction with others and the likely application of learning to practice. Open comments were invited. Results: 170 participants from 36 countries and 6 continents took part from 2018 to 2021 (99 doctors, 36 physicists, 32 radiation therapists/RTTs and 3 others). 141 (83%) participants responded to the post-program survey. Average weightings for responders' views on whether pre-determined learning objectives were met ranged from 4.30 to 4.61 on a 5-point scale (1 = 'not met at all' and 5 = completely met). For the question addressing potential value of learning for application to their workplace, 124 of 130 (95%) of responders indicated that FLIRO would be 'very useful' or 'extremely useful'. Conclusion: Initial evaluation of the FLiRO program supports its continuation and expansion with ongoing evolution based on emerging evidence around leadership education and participant feedback.
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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 | 0.020 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".