Performance of respiratory therapy programs in the Saudi Respiratory Care Licensure Examination: Cross-sectional national results
Bibliographic record
Abstract
Background: Recently, there has been an increase in the number of respiratory therapy programs. However, a national consensus is lacking in intended learning objectives, appropriate teaching methods, and suitable assessment tools. Consequently, variations in outcomes among these programs are expected. Aim: To evaluate the performance of respiratory therapy programs in the Saudi Respiratory Care Licensure Examination (SRCLE). Methods: The SRCLE data were retrieved from the Saudi Commission for Health Specialties (SCFHS) database as of 18 March 2024. The datasets included the number of applicants, overall passing rates, maximum scores, and average scores. Data were categorized based on academic institution, including the type of university (governmental or private), nationality, gender, passing status, number of exam attempts, and year of examinations. Performance comparisons were conducted based on gender and year of examinations. Results: The database from the SCFHS shows that 1305 examinees underwent the SRCLEs from the second quarter of 2021 to the first quarter of 2024. Females accounted for 46% of the total, while Saudi examinees made up 97% of all applicants. The overall passing rate stood at 96%. The average score was 613, with the highest score recorded being 740. Notably, there was no significant difference in performance between males and females (p = 0.299). However, there was a considerable variance in performance based on the year of examination (p = 0.024). Conclusion: The existing data demonstrates that most respiratory therapy programs perform well in SRCLE. We found no significant differences based on gender or the type of school attended. Additionally, the performance of these programs has remained consistent over the years.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".