Sci‐Fri AM: Quality, Safety, and Professional Issues 07: Global Medical Physics Efforts: Closing the Gap
Bibliographic record
Abstract
Purpose: There is an increasing awareness of the disparity in Medical Physics needs between high income countries (HICs) and low‐to‐middle income countries (LMICs). This is especially evident with the growing incidence of cancer in LMICs. Projections from the recent Lancet Oncology Commission on Expanding Global Access to Radiotherapy indicate that an additional 22,000 Medical Physicists will be required by 2035 to provide uniform access to radiation therapy globally. This paper addresses possibilities and challenges associated with closing the Medical Physics gap between HICs and LMICs. Methods: Medical Physics and Oncology related organizations involved in providing support to enhance cancer therapy in LMICs were reviewed, especially as related to education, training and human resource development. Results: More than 35 organizations involved in addressing the cancer crisis in LMICs were found. Of these, 16 involve Medical Physics activities, with 7 being specific Medical Physics‐related organizations. Ten of the 16 are involved in some LMIC activities with 6 having a major emphasis on LMIC contexts. Conclusions: The development of Medical Physics human resource capacity is a major challenge for LMICs. Fifty‐five countries have no radiation therapy capabilities and by implication no capacity to train Medical Physicists. Overt attention with structured and altruistic actions by HIC contexts will help make inroads into the LMIC needs. Clear options throughout career structures in support of global health considerations combined with strong partnerships between interested parties in HICs and LMICs will enhance the development of safe and resource‐appropriate strategies for advancing Medical Physics capabilities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.056 | 0.014 |
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".