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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".