Global health in Canadian radiology residency training: Program directors' perspectives and the current state of implementation
Bibliographic record
Abstract
Radiology is integral to global health, yet low-resource settings face major workforce disparities. Global health radiology (GHR) seeks to improve imaging access, quality, and training in underserved regions, and faces increasing interest among Canadian trainees. However, limited data exist on Canadian radiology program directors' (PDs) perspectives, which are central to shaping educational policy. This study explored PDs' views on GHR relevance, resident and faculty engagement, barriers to integration, and future directions. A bilingual online survey was distributed to PDs of all 16 accredited Canadian radiology residency programs. The survey assessed attitudes toward GHR, teaching practices, faculty involvement, barriers, and resident interest. Descriptive statistics were used for quantitative analysis, complemented by qualitative responses. Fourteen PDs responded (87.5 %). Most (72 %) rated radiology's contribution to global health as essential or very important, and 79 % considered GHR a valid academic career path. While 86 % deemed formal GHR training at least moderately important, only 29 % of programs included it in curricula, and 21 % offered GHR electives. Faculty involvement was limited, with 50 % reporting some engagement, typically less than one half-day per week. Resident interest is rising, with 57 % of PDs noting increased demand over the past five years. Barriers included lack of faculty expertise (71 %), curriculum time constraints (64 %), and absence of national guidelines (57 %). Canadian PDs recognize the importance of GHR but implementation remains limited. Expanding faculty capacity, embedding GHR themes into existing curricula, and developing national frameworks may help bridge the gap between interest and practice, advancing socially responsible radiology training in Canada.
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 | 0.024 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.012 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".