An Evaluation of the 2020 Change to the Saudi Emergency Residency Program Assessment
Bibliographic record
Abstract
Background Several changes have been made to the assessment component of Saudi residency training programs. Among those is the implementation of three examinations over the course of the year. Aim We aimed to explore the emergency residents’ perspective on the change in the number of examinations, and the impact of such changes in terms of time management, knowledge gain, and social life. Methods This cross-sectional study was carried out from September to October 2022, using an electronic survey targeting emergency board trainees. Results One hundred and nine emergency residents enrolled, of whom 64.2% were male. The majority, 45%, were from the central province. Junior-level residents (R1) represented 26.6% of the sample, while R2 (second year) comprised 18.3%, R3 (third year) comprised 38.5%, and 16.5% were senior (R4) level. More than half of the participants, 56 % (n=61), did not support the change from one to three examinations and believed that it had a negative influence on knowledge gain and clinical skills. The influence of the change on time management stands out as a negative impact, in addition to its impact on social life and annual leave arrangements. Conclusions The support for three examinations throughout the year was low; a contributing factor to this may be the sudden changes effected by those tests on training and time management. A re-evaluation of testing culture and involving residents in decision-making might generate acceptance.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".