Clinical specialist radiation therapists (CSRT): Creating capacity while improving quality of care.
Bibliographic record
Abstract
124 Background: Ontario’s cancer system faces many challenges, including a rising incidence of cancer, aging population, increasingly complex cancer treatment, and health human resource (HHR) constraints. In response, Cancer Care Ontario and the Ontario Ministry of Health and Long Term Care collaborated on a project to assess whether a new advanced practice radiation therapist role – the ‘Clinical Specialist Radiation Therapist’ (CSRT) – could enhance access to high quality, innovative care by optimizing the use of HHR. Methods: This innovative model of care aims to enable radiation therapists with advanced training and accreditation (CSRTs) to assume responsibility for certain activities traditionally performed by radiation oncologists (ROs) while maintaining and improving the quality, accessibility and efficiency of radiotherapy (RT) for patients. To assess CSRTs’ impacts standardized metrics, including efficiency (access, wait times (WTs), team function) and quality (new/enhanced services, patient experience) measures, were used. Results: Currently there are 24 CSRTs in 9 of 14 regional cancer centres. 2014/15 data demonstrated that CSRTs can improve the efficiency of referral processes and clinic operations, decrease WTs, and increase capacity (2-28 additional patients seen in clinic/month). Optimized team function and time savings (5-66 RO hours/month) have been achieved through CSRTs’ assumption of certain patient assessment and treatment planning activities. Efficiencies have improved patient experience by facilitating quicker, more coordinated flow through the RT process, and greater continuity of care. Further, CSRTs have enhanced access to high quality RT, through > 75 innovative initiatives (rapid access clinics, telemedicine consults). Conclusions: The CSRT role demonstrates how innovative models of care can improve patient access to high quality cancer care. With 24 CSRTs implemented, opportunities for analysis of factors which facilitate achievement of maximal impact and position sustainability exist. Such investigations could inform the refinement and further implementation of CSRTs in Ontario and other jurisdictions, improving patients’ access to RT more broadly.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".