Can-AP: Informing a system level model for advanced practice implementation in radiation therapy through global experience
Bibliographic record
Abstract
INTRODUCTION: This work sought insight from countries at various stages of advanced practice radiation therapy (APRT) implementation, focusing on five system-level elements: practice framework, regulatory structure, educational preparation, certification model, and role implementation. METHODS: A key informant survey targeted six national academic APRT leaders: Australia, Canada, Singapore, United Kingdom, United States, and France. Likert scale and ranking items considered the order of approach and progress in proposed elements, with additional insight sought in open-ended items. RESULTS: Practice framework was the most well-addressed element; 4/6 (66.7%) informants believing it was at least "well underway". The development of a regulatory structure was the least well advanced. Singapore's informant believed their country to be furthest along overall, while France was perceived to have progressed the least. Informants tended to believe regulatory structure should be addressed earlier than it had been, while certification could come later, though some noted broader system-level political considerations that might impact advancement. Four informants (66.7%) noted element interreliability, seen to challenge efforts to pursue any individual element alone. All informants noted the value of therapist-led committees to advance elements, and those earlier in APRT journeys (Singapore, United States, France) noted the value of practice frameworks from Canada and the United Kingdom in informing work. Australia, Canada, and the United States proposed adding economic analysis of APRT as a sixth element. CONCLUSION: APRT integration demonstrates jurisdictional commonalities and nuances that can inform a system-level model. A 'Can-AP' model is proposed that integrates elements and builds clarity, acceptance, credibility, competence, viability, and a common implementation and process reporting standard for global APRT and other health professions exploring advanced practice.
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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 | 0.082 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".