Gathering evidence on preparation for advanced practice in radiation therapy: An international focus group synthesis
Bibliographic record
Abstract
Purpose: Advanced Practice Radiation Therapist (APRT) roles are expanding globally, yet educational preparation approaches vary significantly across jurisdictions. This study synthesized key multidisciplinary interest-holder perspectives to identify essential considerations for APRT preparation. Methods and Materials: Focus group interviews were conducted via videoconferencing, including practicing APRTs, gatekeepers, educators, and institutional radiation oncology leaders. Semi-structured discussions explored educational content, clinical training, assessment methods, and implementation factors. Data underwent inductive thematic analysis utilizing Braun and Clarke's six-step approach, followed by concept mapping to organize findings into an integrated thematic structure. Results: Four focus group sessions involving 33 participants from ten countries across North America, Europe, Asia, and Australia were conducted between October 2024 and January 2025. Five interconnected themes representing key considerations for APRT preparation emerged: (1) content of educational preparation including the four APRT pillars of clinical practice, research, leadership, and education; (2) aligning preparation outcomes with the intended scope of practice, particularly autonomous decision-making and critical thinking; (3) integrating diverse learning processes combining traditional knowledge acquisition with experiential clinical training; (4) key requirements for preparation, including master's-level education and structured clinical apprenticeship under direct supervision; and (5) critical influence of broader system context on transportability and sustainability across healthcare environments. Participants endorsed master's-level academic preparation combined with extensive clinical experience, drawing parallels to physician training where apprenticeship builds specialist competencies beyond formal academics. Conclusions: This internationally derived, multidisciplinary interest-holder synthesis provides foundational guidance for APRT curriculum development, competency standards, and professional credentialing. While these findings offer important direction for standardizing preparation approaches, future research involving broader multidisciplinary interest-holder groups, implementation studies, and outcomes evaluation is necessary to validate and extend these preliminary findings.
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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 | 0.118 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.017 | 0.011 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".