The scholarly radiation therapist. Part two: developing an academic practice—the Princess Margaret Hospital experience
Bibliographic record
Abstract
Abstract Part two of this two-part series presents the results of a departmental initiative implemented in 2003 at a large urban cancer centre, Princess Margaret Hospital (PMH) in Toronto, Ontario, Canada. This new model for radiation therapists was called Advanced Integrated Practice (AIP) and was developed, in part, to encourage and promote scholarship within radiation therapy. The AIP model incorporated integrated clinical specialty roles designed to blend exemplary clinical practice with focused academic activities. This paper discusses an evaluation of the AIP model undertaken to obtain a formal measure of how the model had evolved, how the radiation therapists and other stakeholders were responding to the new model, whether the initial outcomes were realized and to create plans for further development of the design. The evaluation utilized a mixture of traditional qualitative research methodologies such as focus groups, quantitative surveys and a variety of other available measurable outcomes. Outcomes from the model included increased opportunities for diverse roles that incorporated an element of academic practice and augmented career choice and scope for radiation therapists. In addition, academic output and research work also increased within the department. Lessons learned from the implementation and evaluation of the model are shared, and the authors offer some suggestions to increase scholarly activity within the profession.
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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 | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| 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".