The Clinical Specialist Radiation Therapist (CSRT): A case study exploring the effectiveness of a new advanced practice role in Canada
Bibliographic record
Abstract
INTRODUCTION: The Clinical Specialist Radiation Therapist (CSRT), is a new advanced practice (AP) role for radiation therapists (RTTs). Following training, education and evaluation, the CSRT performs specific duties in autonomous ways, making advanced clinical decisions in their area of specialization. This case study examines the CSRT's impact on quantity (i.e., increasing capacity), improving quality and stimulating research and innovation. METHODS: Between 2007 and 2016, 23 CSRTs worked in 10 cancer centres in various AP position. A standardised metrics package, focusing on wait-times, patient volumes, patient throughput, time-savings, quality initiatives, satisfaction, research and innovation was developed and used to collect qualitative and quantitative data. Data were self-reported by the CSRTs but electronic databases, pre/post-studies, surveys and interviews were also used. RESULTS: Quantity projects (n = 76) related to patient volumes, wait-times, patient throughput and time-savings increased capacity and allowed more patients to enter the system. The presence of a CSRT allowed, on average, 13 additional patients (either new or re-treated) to be seen, at their respective cancer centre, per month. An average of 1.4 yearly quality improvement initiatives were led by each CSRT, which contributed to improvements in quality of care and satisfaction. CSRTs demonstrated a high level of involvement in research, innovation and knowledge translation activities, either as leaders or part of interprofessional teams. CONCLUSION: CSRTs positively impact quantity (capacity of the system), quality, research and innovation. Future efforts include permanent and sustainable team integration, practice standards, formal and comprehensive educational preparation, and approaches to consistent, valid assessment of AP in radiation therapy.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.023 | 0.007 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".