Bibliographic record
Abstract
A workforce comprising medical radiation technologists (MRTs), radiologists, nuclear medicine specialists, medical imaging physicists, and other medical staff is essential for the delivery of medical imaging services. There is wide variation in the numbers of staff in each profession in Canada, ranging from 45 positions located in 6 provinces for medical imaging physicists to 25,000 positions located across all provinces and territories for MRTs. Data from 2022–2023 suggest that the number of full-time staff has not grown at the same pace as the volume of exams. Sufficient staffing is needed to ensure the sustainable operation and delivery of medical imaging services that is supported by investment in the workforce through equitable education and training opportunities. According to a Canadian Medical Imaging Inventory (CMII) report on wait time strategies, staffing shortages may extend wait times and may lead to disruptions in service delivery. Managing the growing demand for imaging services and clearing the backlog of exams deferred during the pandemic has exacerbated existing staffing shortages and contributed to increased workloads and decreased staff well-being. The adoption of supportive tools and technologies — such as clinical decision support tools, automated order entry, and AI-driven solutions — may assist the workforce by creating efficiencies, improving image quality, and increasing access to medical imaging.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.052 | 0.013 |
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".