AAPM Task Group 298: Recommendations on certificate program/alternative pathway candidate education and training
Bibliographic record
Abstract
Entry into the field of clinical medical physics is most commonly accomplished through the completion of a Commission on Accreditation of Medical Physics Educational Programs (CAMPEP)-accredited graduate and residency program. To allow a mechanism to bring valuable expertise from other disciplines into clinical practice in medical physics, an "alternative pathway" approach was also established. To ensure those trainees who have completed a doctoral degree in physics or a related discipline have the appropriate background and didactic training in medical physics, certificate programs and a CAMPEP-accreditation process for these programs were initiated. However, medical physics-specific didactic, research, and clinical exposure of those entering medical physics residencies from these certificate programs is often comparatively modest when evaluated against individuals holding Master's and/or Doctoral degrees in CAMPEP-accredited graduate programs. In 2016, the AAPM approved the formation of Task Group (TG) 298, "Alternative Pathway Candidate Education and Training." The TG was charged with reviewing previous published recommendations for alternative pathway candidates and developing recommendations on the appropriate education and training of these candidates. This manuscript is a summary of the AAPM TG 298 report.
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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 | 0.095 | 0.135 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.027 | 0.013 |
| Insufficient payload (model declined to judge) | 0.019 | 0.031 |
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".