A new model of care: An advanced practice radiation therapy role.
Bibliographic record
Abstract
131 Background: Due to the rising incidence of cancer, increasing complexity of cancer treatment and growing resource constraints, there is demand for innovative interprofessional models of care. Cancer Care Ontario (CCO), in collaboration with Ontario’s Ministry of Health and Long-Term Care, launched the Clinical Specialist Radiation Therapist (CSRT) project to investigate a new advanced practice (AP) radiation therapy (RT) role. Methods: A series of pilot phases commenced in 2004. A system wide implementation phase began in 2010. The overall goal of the project was to enable CSRTs to assume responsibility for certain key radiation medicine activities, optimize RT team functioning, and improve quality of care. This was to be achieved by applying advanced clinical, technical and professional RT competencies. A project team coordinated assessment of new position proposals, implementation activities, and data collection. Work towards formalizing the role is being conducted through partnerships with cancer centre administrations and the national professional association for RTs. Results: Currently there are 17 CSRTs in 6 of 14 Ontario cancer centres, practicing in palliative and disease-site specific positions. The provincial total will be 24 CSRTs in 10 centres by Fall 2014. Data show a major impact on patient throughput and wait times, with significant increased capacity in some clinics. CSRTs’ impact within their departments is being evaluated with regards to: 1) capacity building, 2) quality of care, and 3) knowledge translation (KT). In terms of role formalization, 6 positions are now considered permanent within their centres and the first iteration of a national certification process is anticipated for fall 2014. Conclusions: The CSRT project is aligned with CCO's priorities of improving Ontario’s cancer system performance by implementing innovative models of care and providing high quality care. This jurisdictional implementation project has demonstrated that an AP RT role can be successful in addressing system pressures and improving quality of care and innovation in Radiation Medicine. Further work is necessary to develop and formalize this AP role and leverage learnings for national implementation and future models of care work.
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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 | 0.016 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".