Bibliographic record
Abstract
Education programs for medical physicists can be limited by local teaching resources and available technologies. Furthermore, modern imaging and therapy technologies continue to evolve at a rapid rate and require continuous learning support for their safe and effective implementation in the clinic. Mentorship provides an important resource in support of filling the educational gaps and the knowledge updates required for the development of new techniques and the implementation of new technologies. A structured mentorship process can be a useful resource at any point during a medical physicist’s career, although the specific purpose of the mentorship will likely vary depending on when in one’s career this occurs. This review provides a description of the various stages of a structured mentorship program along with some guidance on how to implement a sustainable and successful mentorship activity. Seven mentorship stages are described including ‘defining the purpose of the mentorship’, ‘finding mentor / mentee partners’, ‘setting the scene’, ‘engagement’, ‘evaluation’, ‘redefinition’, and ‘separation’. Some etiquette guidelines are proposed including some suggested dos and don’ts. In some regions, there may be a dearth of nearby appropriately experienced mentors. Mentoring virtually using internet technologies may provide a good alternative. The results of an on-line global survey related to virtual mentoring are summarized. In conclusion, structured mentorships may provide a very useful resource for medical physicists at any stage of their careers. Understanding how best to navigate such mentorship programs will maximize their benefits.
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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 | 0.024 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.017 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".