Global health in Canadian anesthesiology residency programs: an online survey of opportunities and barriers
Bibliographic record
Abstract
BACKGROUND: Global health disparities exist in all geographical locations, including high-income countries like Canada. An understanding of global health is useful for all physicians as it can allow them to better understand and address social determinants of health, with hopes of providing comprehensive care to all patients. Global health inequities in anesthesia remain underexplored, even though the field is closely tied to pain management and surgical care—two facets of healthcare that are critical to global health. We aimed to assess the extent of global health opportunities in Canadian anesthesiology residency programs, the attitudes of anesthesiology program directors and residents toward global health training, and identify barriers to participation in global health within Canadian anesthesiology residency programs. METHODS: A cross-sectional online survey was conducted using a self-administered questionnaire. The total potential sample size consisted of 17 anesthesiology program directors and 694 residents across all Canadian anesthesiology programs. Data were collected via email between September–November 2023, with two reminder emails at two-week intervals. Survey responses were analyzed using descriptive statistics. Inferential statistics analysis (Fisher’s exact test) was used to compare answers from program directors and residents. The Likert scale answers were considered ordinal data. RESULTS: We received 76 completed surveys—11 from program directors and 65 from residents. The program director response rate was 70% and resident response rate was 10%. 18% of program directors identified having a global health curriculum in their program. 82% of program directors considered it important to understand global shortages in anesthesiology, and 81% of residents were interested in incorporating global health initiatives in their programs. The only statistically significant difference between program director and resident responses was in the perception of financial barriers to global health (p = 0.04). CONCLUSION: Canadian anesthesiology program directors and residents show strong interest in global health education. Addressing barriers, especially financial ones, and integrating sustainable partnerships, global health EPAs, and funded rotations could enhance training. These findings may guide other programs internationally in developing global health initiatives.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".