The ideal applicant to emergency medicine residency programs in Saudi Arabia; Program directors’ view
Bibliographic record
Abstract
Objective: Emergency medicine (EM) is considered a competitive specialty worldwide with an acceptance rate of 57% in Canada, but it is even more competitive in Saudi Arabia with 18.7% acceptance. Factors that may influenced the applicant’s acceptance into residency programs included letters of recommendation, interview performance, research experience, and gender. This study aims to determine the factors playing a role in applicants matching to EM residency programs in Saudi Arabia from the viewpoint of program directors. Methods: A pilot study was done using a self-administered questionnaire distributed to EM residency program directors (PDs) in Saudi Arabia during the period of 16-21 November 2021. The data were analyzed using SPSS, and all ethical considerations were observed. Results: Twenty-seven PDs participated in the study, 19 (70.4%) were male, and most were former PDs (59.3%). The most crucial aspect in the applicant’s acceptance was the excellent impression in the interview (4.00 ± 1.00). The most crucial aspect of recommendation letters was a recommendation from a program director (29.6%). In addition, total duration of electives in EM (40.7%) was important, quality in EM research (29.6%) played a critical role, and professionalism (29.6%) was the factor sought during the interview. The PD’s gender or status or the region of the program did not significantly affect the preference of the applicant’s gender. Conclusion: For those considering EM residency programs in Saudi Arabia, the chance of getting accepted can be increased by getting a recommendation from a program director, increasing the duration of electives in EM, focusing on the research quality, and showing professionalism during the interview.
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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 | 0.011 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".