A 5-Year Analysis of Saudi Arabian Applications to Plastic Surgery Residency Training in Canada
Bibliographic record
Abstract
Introduction: The goal of this study is to determine factors associated with acceptance into Canadian plastic surgery (PS) residency programs by Saudi Arabian Medical Graduates. Methods: This is a cross-sectional study targeting eligible Saudi Arabian applicants to any Canadian PS residency program between 2017 and 2022. Collected data included demographics, education/licensure, electives in Canada, and letter of reference. The 2 main outcomes analyzed were “invited for an interview” and “offered a residency position.” Results: We reviewed 52 applications. Of these, 18 applicants received an invitation for an interview and 8 were offered a residency training position. Significant variables associated with receiving an invitation for an interview were completing an elective in Canada ( P = .016), having a master's degree ( P = .012), and completion of an English test ( P = .032). The variables most likely to influence receiving a residency position offer are completion of elective training in Canada ( P = .004) and receiving a letter of reference from a Canadian plastic surgeon (95% CI: 0.77 to 76.69: OR: 8.90). Conclusion: Completion of an elective rotation in Canada and obtaining Canadian letters of reference were found to be the most important factors for Saudi Arabian physician applicants to be accepted into a Canadian PS residency program. Other factors that were less critical but improved their chances of being considered include completion of an English assessment test and having a master's degree. This study offers valuable guidance for any Saudi Arabian candidates interested in PS residency in Canada and may aid Canadian programs in their assessment of potential residents.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".