Radiation therapist research capacity and output: a multicentre retrospective study of authorship, collaboration, and institutional strategies (2013–2022)
Bibliographic record
Abstract
Purpose: Radiation Therapist (RTT) research culture is essential for driving innovation and informing evidence-based practice. This study aimed to assess RTT research output and institutional capacity-building initiatives across international clinical academic cancer centres. Methods: This was a retrospective analysis of RTT research activities and capacity-building initiatives from 2013 to 2022 at three centres located in Canada (CA), the Netherlands (NL) and the United Kingdom (UK). Data was collected on research output by identifying all RTT author publications (first, second, or senior author). Institutional capacity-building initiatives were captured from each centre and described using full-time equivalents (FTEs). A qualitative analysis was conducted on all RTT publications to identify common research topics. Results: Over the 10 years, the total number of RTT-authored publications was 445 across the centres (CA:291; UK:79; NL:75). RTTs as first authors ranged from 14.7 % to 44.3 % and RTTs as senior authors ranged from 0 % to 27.8 % of publications. Centres with increasing FTEs demonstrated increasing research productivity, with publications changing from 21 to 34 in CA and from 3 to 15 in the UK centre. Multidisciplinary collaboration was common among all centres. Prominent RTT research themes included technological applications, RTT professional development and quality assurance, clinical outcomes, dosimetry, and patient care. Common strategies to build research capacity included educational initiatives, the creation of dedicated research roles, and promoting research dissemination. Conclusion: This study highlighted the contributions of RTTs to radiation oncology research and how a comprehensive approach to building research capacity results in high RTT research output and collaboration.
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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 | 0.063 | 0.203 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.026 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.008 |
| 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".