MRI Service Delivery in Canada: Units, Exam Volumes, and Medical Radiation Technologist Staffing
Bibliographic record
Abstract
What Is the Issue? Canada’s Drug Agency (CDA-AMC) received a request to explore the relationship between full-time equivalent (FTE) medical radiation technologists (MRTs) and MRI exam volumes nationwide. Workforce availability — particularly the number of MRTs — has been identified as a key factor that may impact the delivery of MRI services. Although a recent study examined this relationship within a single province, no national-level research has explored how MRT staffing relates to MRI exam volumes across Canada. Understanding this relationship could help inform operational planning for jurisdictions and health care facilities, particularly in aligning workforce capacity with service demand. What Did We Do? CDA-AMC analyzed site and unit-level data gathered through the 2022–2023 Canadian Medical Imaging Inventory (CMII) National Survey. The goal was to examine the relationship between the number of FTE MRTs per MRI unit and the average annual volume of MRI exams. To ensure comparability across facilities, sites were grouped based on the number of MRI units onsite. What Did We Find? At a national level, most sites (69%) reported 4 or more FTE MRTs per MRI unit. This trend was consistent across sites with 1 to 3 MRI units. Among the 3 sites with 4 or more MRI units, 2 reported having 3 FTE MRTs per unit. On average, sites with higher numbers of FTE MRTs per MRI unit tended to perform more exams annually. Although higher staffing levels are associated with greater exam volumes, throughput is also influenced by a range of other factors, including exam complexity, research and teaching use, scheduling models, and broader policy and funding decisions, that shape how MRI services are delivered. What Does This Mean? This report provides the first national-level evidence-based analysis to support workforce planning for MRI service delivery. These findings can help decision-makers in the following ways: to estimate appropriate staffing levels by showing how staffing needs change based on factors such as site size and exam volume to demonstrate the operational impact of adequate staffing, with higher staffing levels correlated with increased annual exam performance, suggesting that well-staffed medical imaging departments are better positioned to meet demand to guide more informed planning at both individual facility and health system levels, especially when considering expansion or resource reallocation.
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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 | 0.004 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.010 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".