Challenges Facing Emergency Medicine Residents in Saudi Arabia: A Cross-Sectional Study
Bibliographic record
Abstract
ABSTRACT Background: Challenges related to the residency programme differ according to residents’ roles, interactions, culture, responsibilities and expectations. Aim: This study aims to explore the challenges faced by emergency medicine physicians during their residency programme. We also aim to investigate the influence of several demographic variables on their training experience. Method: This is a cross-sectional study, conducted in Saudi Arabia from June, 2021 to July 2021, using a survey designed by the author. Results: The total number of participants was 37. Of these, 24.3% (n=9) were R1 residents, 10.8% (n=4) were R2, 35.1% (n=13) were R3, and 29.7% (n=11) were R4. While most of the participants (89%) clearly understood which reference to use for studying, only 56.7% had a clear understanding of how to study for the exams. Reading club was advocated by 72% of participants, and only half the participants had a positive perception of leading, preparing, and discussing topics during academic activity. Of all the residency levels, R3 residents were the most supportive of having expert physician guidance during ED procedures, p=0.04. Other factors given more importance by R3 residents than by other levels were mentorship, p=0.051, and having a course review for the exam, p=0.001. Conclusion: This study uncovers several challenges reported by participants from different residency levels. We noted that the R3 training level, being a period of transition from junior to senior level, is a significant period requiring more attention; more emphasis on mentorship and reading club is advocated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".