Established advanced practice roles in radiation therapy: A scoping review
Bibliographic record
Abstract
Advanced practitioners are healthcare professionals that are highly skilled with a particular area of expertise. These roles have been successfully implemented in many healthcare settings, improving efficiency of the service, as well as enhancing the standard of care received by patients. Although advanced practice roles have been implemented in some radiation therapy departments, their implementation have yet to be facilitated in the majority of countries. The purpose of this review is to scope the literature available regarding established advanced practice roles in radiation therapy. The PRISMA strategy for the identification of relevant literature was adhered to. Two data bases, EMBASE and PubMed, were searched using combinations of the key words 'Advanced', 'Practice', 'APRT', 'Radiation', 'Therapy' and 'Radiotherapy'. Exclusion criteria were applied, and citation lists were also screened for additional relevant sources, including grey literature sources. A total of 35 relevant sources were identified that discussed advanced practice radiation therapy roles in the United Kingdom, Singapore, Canada, Australia and the USA. Means of role establishment and scope of practice were defined, and a number of advantages and challenges for advanced practice radiation therapist roles were identified. There are many benefits of implementing advanced practice roles in radiation therapy departments. Though the implementation of these roles can be challenging, the existing evidence indicates that it would be beneficial for the patient, the radiation therapist and the department as a whole. A more systematic approach, including reporting of quantitative outcomes may assist in the more widespread implementation of these roles.
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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 | 0.027 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.018 | 0.024 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 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".